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Study on the societal acceptance of Urban Air Mobility in EuropeMay 19, 2021
Confidential and proprietary. Any use of this material without specific permission of EASA is strictly prohibited.
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This study has been carried out for EASA by McKinsey & Company upon award of a specific contract implementing a running multiple framework contract for the provision of consultancy services. Consequently, it does not necessarily express the views of EASA itself, nor should it be relied upon as a statement, as any form of warranty, representation, undertaking, contractual, or other binding commitment upon EASA. Ownership of all copyright and other IPR in this material including any documentation, data and technical information, remains vested to EASA. All logo, copyrights, trademarks, that may be contained within, are the property of their respective owners. Reproduction of this study, in whole or in part, is permitted under the condition that this Disclaimer remains clearly and visibly affixed in full at all times with such reproduced part. This study has measured the attitude of the EU society towards UAM early 2021, well in advance of future deployment in EU cities foreseen around 2024-2025. The results have been generated with best effort at this point in time, however public perception may change over time once citizens are exposed to actual UAM operations.
Further information and the full survey insights are available at easa.europa.eu/UAM
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Executive Summary
New technologies such as the enhancement of battery technologies and electric propulsion as well as major investments made into start-ups are enabling the development of new vertical take-off and landing Urban Air Mobility (UAM) aircraft. Thus, Urban Air Mobility – defined as an air transportation system for passengers and cargo in and around urban environments – may be deployed in Europe within three to five years, offering the potential for greener and faster mobility solutions.
As several projects and demonstrations are under way, it is time for the European Union, and for national and local authorities to prepare the framework that will enable this new mode of transport and give Europe the chance of establishing itself as one of the first movers in this field at a global level.
Citizens’ and future UAM users’ confidence and acceptance will be critical to success. As part of the preparation of an adequate regulatory framework, the European Union Aviation Safety Agency (EASA) therefore conducted this comprehensive study on the societal acceptance of UAM operations across the European Union. The study was carried out together with the consulting firm McKinsey & Company between November 2020 and April 2021. Full details of the report can be found on the EASA website.
Based on thorough research, literature review, local market analysis, surveys and interviews, the study examined the attitudes, expectations and concerns of EU citizens with respect to UAM and revealed interesting insights, some unexpected. The survey results were very homogeneous among all those surveyed across the EU and in all socio-economic categories. They can be clustered into 10 key take-aways:
1. EU citizens initially and spontaneously express a positive attitude toward and interest in UAM; it is seen as a new and attractive means of mobility and a majority is ready to try it out;
2. The notion of general/public interest is a determining factor for acceptance: use cases for the benefit of the community, such as medical or emergency transport or those connecting remote areas, are better supported than use cases satisfying individual/private needs;
3. The main benefits expected from UAM are faster, cleaner and extended connectivity;4. However, when encouraged to reflect upon the concrete consequences of potential UAM operations in their
city, EU citizens want to limit their own exposure to risks, in particular when related to safety, noise, security and environmental impact;
5. Safety concerns come first, but the study also shows that citizens seem to trust the current aviation safety levels and would be reassured if these levels were applied for UAM;
6. Noise is the second main concern expressed; the study indicates that the level of annoyance varies with the familiarity of the sound, with familiar city sounds at the same decibel levels being better accepted; it also confirms that the distance, duration and repetition of the sound impacts its acceptance;
7. UAM is seen as a good option to improve the local environmental footprint, through reduced urban traffic congestion and better local air quality; but at the same time citizens express major concerns about UAM’s impact on wildlife;
8. The results also demonstrate a limited trust in the security and cyber security of UAM, requiring threat-prevention measures;
9. The integration of UAM into the existing air and ground infrastructure must respect residents’ quality of life and the cultural heritage of old European cities;
10. Finally, local residents and authorities feel directly affected by the deployment of UAM and want to engage and play an active role in its implementation.
3Executive Summary
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The study results show that EU citizens are calling for active and pre-emptive measures from competent authorities.
In addition to mitigating risks related to safety, security, noise and environmental impact, these measures are expected to ensure that UAM will be a common benefit to all of society by offering affordable, integrated and complementary mobility. By providing transparent and timely information and guidance, the authorities at all levels – local, national and European – have the chance to consolidate public acceptance of UAM.
By 2024-25, UAM may be a lived reality in Europe. The EU has only a few years, and a unique opportunity, to prepare for the smooth transition of European cities, and also of the European aviation system, towards the mobility of the future.
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Table of contents
Table of content
Table of content 5
Definitions 6
Introduction 7
1. Research and literature review 11
1.1 Literature review 12
Methodology 13
Summary of insights 14
1.1.1 UAM use cases found in literature 16 1.1.2 Expected challenges identified in literature 16 1.1.3 Societal acceptance factors identified in literature 17
1.2 Industry status and projection 21
Overview 21
UAM vehicle types 22
1.2.1 Aircraft and Use Cases 22 1.2.2 UAM stakeholder environment 27
1.3 UAM high level societal benefits and risks 30
1.3.1 Benefits based on market models, literature and expert interviews 30 1.3.2 Risks and acceptance based on literature and expert interviews 32
2. Assessment of urban European target markets 35
2.1 Use case prioritisation 36
2.2 Target market identification 42
3. Survey-based assessment of public acceptance of UAM in the EU 49
3.1 Survey methodology 50
3.1.1 Quantitative survey methodology 50 3.1.2 Qualitative survey methodology 57 3.1.3 Noise test methodology 58
3.2 Survey results (10 key findings) 61
3.2.1 A positive initial attitude to UAM throughout the EU 61 3.2.2 Strong support for use cases in the public interest 66 3.2.3 Top 3 expected benefits: faster, cleaner, extended connectivity 68 3.2.4 Top 3 concerns: safety, environment/noise and security 70 3.2.5 Safety: existing aviation safety levels are the benchmark 73 3.2.6 Environment: priority is protection of wildlife 75 3.2.7 Noise: acceptable at level of familiar city sounds 79 3.2.8 Security: need to build confidence and trust in citizens 85 3.2.9 Ground infrastructure: must be integrated well 86 3.2.10 Regulatory authorities: must work together at all levels 88
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DefinitionsANSP: air navigation service providerBVLOS: beyond visual line of sightEASA: European Union Aviation Safety AgencyEIS: entry into serviceOEM: Original Equipment manufacturerTransforming vehicle: a vehicle that can drive on the road and fly, e.g. a flying carNASA: National Aeronautics and Space AdministrationUAM: urban air mobilityVTOL: vertical takeoff and landingConjoint analysis: trade-off survey method to evaluate relevance and extend of decision factorseVTOL: electric vertical takeoff and landingUAS: Unmanned Aircraft System, i.e. an unmanned aircraft, i.e. without a pilot on board, and the equipment to control it remotelyManned / unmanned aircraft: an aircraft with a pilot/ without a pilot on boardAutonomous aircraft: an aircraft flying without the assistance of a dedicated pilot
4. Expectations and options for action 93
5. Conclusions 99
Appendix 1 101
Appendix 2 103
Detailed information on target market identification 103 Detailed information on questionnaire structure 122 Detailed information on methodology of quantitative survey question types and questionnaire 126
Appendix 3 154
Bibliography 154
A study on the societal acceptance of Urban Air Mobility in Europe6
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Introduction
Background and context of the study
Congested streets and pollution are already a reality in several European cities, as indicated by the TomTom Traffic Index 2020 and the Air quality in Europe 2020 report by the European Environment Agency. With the population of cities in the European Union set to grow to more than 340 million citizens by 2030, there is a risk of increased pollution and congestion. In this context, local authorities are looking at smarter, greener, more integrated and sustainable mobility solutions.
Urban Air Mobility (UAM) has the potential to respond to these needs. Air transport of goods and people is no longer science fiction and will become a reality in European Union cities soon. Adding a new dimension to urban transportation will allow air transport of goods and people and may also help to make a leap towards smarter and more sustainable cities. Urban Air Mobility is expected to bring environmental benefits as well as advantages for citizens and businesses – notably for commercial or emergency/medical purposes.
A key enabler for the development of Urban Air Mobility solutions was the significant reduction in lithium-ion battery cell costs to 110€/kWh in 2020 from 1000€/kWh in 2010, as well as the increase of cell energy density to approximately 300 Wh/kg from approximately 150 Wh/kg in the same timeframe.1 The experiences gained with the development of electric vehicles in the automotive industry have also influenced the development of UAM globally and in Europe. The European industry has played a leading role in the development of UAM since the first flight of a manned eVTOL proof-of-concept by Volocopter in 2011. There are also several other European companies developing UAM aircraft at the moment, for example Airbus, Ascendance, Lilium, Pipistrel, Quantum Systems, and Tecnalia.
Objective of the study
This breakthrough in urban mobility needs to be accompanied and supported by relevant measures, in particular an adequate regulatory environment, which would reflect the needs and aspirations of European society and provide a stable and clear framework for the industry. The first step consists in measuring EU citizens’ willingness to accept this new mode of transport and collating their possible concerns and expectations, for instance related to safety, security, privacy and environmental impact.
The European Union Aviation Safety Agency (EASA) launched a comprehensive study on the societal acceptance of UAM across Europe in November 2020. The study included research work, literature review, as well as a survey with around 4000 residents of six European cities. These survey cities – Barcelona, Budapest, Hamburg, Milan, Paris and the cross-border region Öresund – were identified as potential target markets for the future deployment of Urban Air Mobility. The quantitative survey was complemented by more than 40 qualitative interviews with focus groups of local, national and European stakeholders as well as by a noise perception study with a group of 20 European residents.
EASA ambition
The study on societal acceptance is only one aspect of EASA’s work to support the deployment of UAM in the EU. EASA’s ambition is to anticipate this new mode of transport and provide an enabling comprehensive regulatory environment, allowing the EU to establish itself as one of the first global movers in this field.
1 Bloomberg NEF
Definitions / Introduction 7
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Work has started and initial actions have been taken. EASA has prepared a number of regulatory documents, the latest one being the first worldwide regulation on U-Space recently adopted by the European Commission. The SESAR JU defines U-space as follows: “U-space is a set of new services relying on a high level of digitalisation and automation of functions and specific procedures designed to support safe, efficient and secure access to airspace for large numbers of drones. As such, U-space is an enabling framework designed to facilitate any kind of routine mission, in all classes of airspace and all types of environment - even the most congested - while addressing an appropriate interface with manned aviation and air traffic control.” The full overview of these documents is provided in Appendix 1.
The results of the study will be considered by EASA in the preparatory work for a future regulatory proposal for the so called high risk operations of the specific category of drones and for operations of the certified category of drones and manned VTOLs in urban environments. They will also serve to raise awareness about UAM across the EU as a means of fostering public adoption.
Scope of the study
The terms ‘Advanced Air Mobility (AAM)’ and ‘Urban Air Mobility (UAM)’ are both in common use. As can be seen in Figure 1, AAM covers passenger and cargo transport as well as other aerial missions in urban, regional, and interregional geographies. UAM can be understood as a subset of AAM, which covers transportation systems that move people or cargo by air in and around urban environments.2 In the absence, as yet, of agreed standard definitions, the term “Urban Air Mobility” is used in the context of this study, as it explicitly refers to the specific context of the operations, i.e. in cities and densely populated environments, and is therefore more easily understood by the general public.
In this report, “urban” is defined according to the functional urban area concept used by Eurostat: “A functional urban area consists of a city and its commuting zone. Functional urban areas therefore consist of a densely inhabited city and a less densely populated commuting zone whose labour market is highly integrated with the city (OECD, 2012)“.
2 https://www.easa.europa.eu/sites/default/files/dfu/easa_drones_section.pdf
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Figure 1: AAM covers a wider scope than UAM
Advanced Air Mobility is a more inclusive definition of UAMIncluded scope in AAM and UAM definitions
Use cases Geographic reach
CargoPassenger Operations Urban Regional Interregional
AAM FAA
NASA
NASA
Deakin Uni.
UAM NASA
SESAR JU
FAA
Deakin Uni.
Jonkoping Uni.
MITRE Corp
UC Berkley
TU Munich
Explicit mentionNo explicit mention
Furthermore, the scope of the study was intentionally limited to:• The transportation systems that move people or cargo by air in and around urban environments for commercial or
emergency service operations. Other use cases, such as infrastructure assessment, surveillance, 5G emissions or state operations (e.g. military, police surveillance) were excluded. The transportation of goods or people is indeed adding an additional risk that may require specific attention;
• Drones and manned VTOL aircraft with electric propulsion systems were the focus for this study. Other vehicles such as traditional helicopters or transforming vehicles (e.g. flying cars or motorcycles) were excluded as the focus should remain on new types of vehicles intended for use in urban airspace;
• A 10 year timeframe, i.e. until 2030: for this reason, the study focused on manned VTOL (i.e. with a pilot on board) for the transport of people, as it appears unlikely that unmanned transport of people in urban environments may take place within that timeframe;
• The European Union, although global developments were taken into account for information purposes.
This report was created based on the best knowledge of the involved parties at the time of writing. However, due to the fast pace of this emerging industry the stated content might be subject to change in the future.
9Introduction
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1. Research and literature reviewThis first chapter contains information on the literature reviewed and the research done to set up the study on societal acceptance in general. This preparatory work ensured that the starting point was the most up-to-date state of science, research and market development.
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1.1 Literature review
To lay the foundations for the study on societal acceptance of Urban Air Mobility (UAM) as well as to collect initial data and information to build the quantitative and qualitative survey, a thorough literature review from two different perspectives was carried out:
1) The UAM market and UAM-related societal acceptance factors2) Insights about relevant societal acceptance factors from adjacent technologies, such as autonomous driving, smart home and other emerging technologies.
The review focused on the UAM market and related societal acceptance factors, as core objectives of this study. Literature reviewed included recent publications, i.e. not older than three years, in English and other European languages, from academia or other publicly-accessible sources.
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Methodology
The methodology used to filter publications was the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. This framework is a systematic process for filtering publications for duplicates, relevance, and eligibility.
Figure 2 shows that this process was applied to all UAM 130 publications identified through search terms (n=60) as well as publications identified through other sources (n=70). As a first step, 19 duplicates were removed from further consideration. The next step involved a check for relevance: here 20 publications were excluded, as they were either published before 2017, did not cover the UAM space at least partially, or did not mention UAM use cases or societal acceptance factors. In the last step only publicly available publications, which are available free of charge, passed the eligibility filter; the others (n=15) were excluded. This left a total of 76 publications for consideration during the detailed UAM literature review (see Bibliography).
16 publications were identified for the review on societal acceptance factors for adjacent technologies, such as autonomous driving and smart home. There were no duplicates, but three publications did not pass the relevance test as they were either published before 2017 or did not cover societal acceptance factors. Another three publications did not pass the eligibility test as they were not publicly available. This left a total of ten publications for the comparison of societal acceptance factors for smart homes and autonomous driving with UAM.
Source: Adapted from The PRISMA Group (2009)
Relevance
Publications identified
Included
Eligible
Articles identified through search terms (n=60)
Additional research findings identified through other sources (n=70)
Articles and research findings after duplicates removed (n=111)
Articles and research findings screened on basis of title and abstract (n=111)
Articles and research findings excluded (n=20)
Articles and research findings included in qualitative synthesis (n=76)
Full-text articles and research findings assessed for eligibility (n=91)
Full-text articles and research findings excluded, with reasons (n=15)
Figure 2: Application of PRISMA framework for UAM literature review
131.1 Literature review
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Summary of insights
As shown in Figure 3, a considerable increase in the frequency of UAM-related publications can be observed between the years of 2017 and 2020.
Figure 3: Origin and timing of publications
It can also be seen in Figure 3 that:• More than a third of the publications included are from academia, with contributions from leading entities such as TU
Munich, Fraunhofer, Massachusetts Institute of Technology (MIT) and the National Aeronautics and Space Administration (NASA). Consultancies and manufacturers within UAM are the other main contributors of recent publications.
• More than half of the included publications originate from entities with their headquarters in Europe, indicating that leading authorities in the emerging UAM industry tend to be based in Europe. The region with the second highest number of publications was North America. However, the relatively small number of publications from Asia and Africa could be related to the focus on publications in English and other European languages.
2017
18
18 19 20
8
25
2021
47%
1%
43Industrygroups
9
Academia
Consultancies
Governments
18
13
8
Manufacturers
Other
62
1
Europe
30NorthAmerica
Asia
7
AfricaOrigin of publications, % HQ region of publishing entity, %
Publications increasing significantly in recent years%
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In order to gain an understanding of the UAM market, literature contributions were evaluated in terms of:
• use cases, • challenges, • societal acceptance factors, • stakeholders, • timelines, • target markets.
The key results of this evaluation are summarised in Figure 4 and more detailed in the following Figures. The use cases most frequently mentioned in publications are air taxis, drone delivery and rescue drones. The main challenges raised are infrastructure and safety. Noise and safety were listed as the major societal acceptance barriers. Entry into service (EIS) timelines differ significantly between piloted and autonomous vehicles, with most certification or EIS for piloted operations being planned for around 2025. Unmanned or autonomous operations are expected to start entering the market not before 2030, according to statements made by the OEMs in the reviewed literature.
Source: Literature review
2
0
6
4
2022 2023 2025 ~2025 ~2030 2030+ ~2035 ~2040
Piloted
Autonomous
…use cases …challenges2 …societal acceptance barriers3
11% Infrastructure
9% Safety
18% Noise
17% SafetyDrone delivery
Rescue drone1
Air TaxiTaxi
1. Transporting a first responder to the location of an accident2. Share of the 173 mentions regarding challenges (multiple per publication possible)3. Share of the 188 mentions regarding societal acceptance (multiple per publication possible)
Number of mentions with regard to expected start of entering the market
Most often named …
Figure 4: Key insights from the literature review
Interest in UAM increased in recent yearsInitially, 130 publications were identified and still 76 of them – published since 2017 – were analysed and considered in the study. More than half of them originate from entities with their headquarters in Europe. The frequency of publications increased recently, showing a growing interest in the topic.
151.1 Literature review
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1.1.1 UAM use cases found in literature
Cases that were found during the literature review can be grouped into five functionally distinct groups.
1) Passenger transport The most frequently mentioned passenger transport use case is the air taxi. Here, passengers will initially use UAM aircraft to travel from one vertiport to another. Eventually it may be possible to hire an air taxi in a street or park close to the starting point and land in a street or park next to the destination. Quickly flying an emergency doctor to the site of an accident is the application mentioned second most frequently.
2) Delivery The most often described use cases are package and food delivery by drones into private gardens or properties, and
package delivery by drones into a central delivery hub. A number of publications also describe the usage of drones for time-critical medical applications, such as the delivery of organs or stored blood.
3) Civil surveillance and other operations The autonomous inspection and/or maintenance of bridges and other infrastructure is expected to be the major
operations use case. Other operations use cases include precision agriculture and the preliminary visual assessment of incident sites, such as assessing the extent of fires and accidents.
4) Sovereign functions The top-ranking application of UAM aircraft in this group is police surveillance. 5) Signal emitting Emitting signals for multimedia applications or internet access was only mentioned in two publications.
1.1.2 Expected challenges identified in literature
Figure 5 provides details on the expected challenges for UAM. 50 of the 76 reviewed publications mentioned challenges, where the leading challenges are related to infrastructure, safety and noise. In this categorisation, while technology covers a large variety of technological solutions, it mostly refers to battery electric propulsion systems and their current limitations in terms of energy density and overall weight. Environmental impact is a term used generically in the reviewed literature and can include a plethora of topics such as noise, visual pollution, air pollution, land use, protection of species and biotopes, climate, natural resources, water and soil.
Societal acceptance, the focus of this study, is not listed among the overall top five challenges for UAM, but is an important dimension for EASA as its role is to serve the general public in its actions.
Infrastructure named as leading challenge in existing literature Based on literature, biggest challen-ges for UAM are expected to be related to infrastructure, safety and noise. Namely:
• Finding suitable locations / buildings
for Vertiports
• Aiming for safety level similar/equal
to commercial aircraft
• Achieving low noise level for better
social acceptance
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1.1.3 Societal acceptance factors identified in literature
Although most publications do not mention societal acceptance as a main challenge, 61 of the 76 reviewed publications mention social acceptance factors. Figure 6 summarises the main insights from literature. Noise and safety are the leading factors mentioned by a large margin. Combined, they constitute 35 percent of all 188 mentions of an acceptance factor within the publications reviewed. Most of the time, safety refers to the safety of an occupant of an air taxi, but it does also include people on the ground. Environmental impact has the same wide definition in literature as for UAM challenges. Increased travel options refer to the provision of an additional mode of transport for a certain route. This metric has diminishing returns as it provides the highest benefit if there is no other option to travel an intended route without UAM, but only contributes a small benefit if there are, for example, four other modes of transport available.
Main societal acceptance factors are comparable to those of other smart technologiesIf comparing acceptance barriers for
Urban Air Mobility with those of other
smart technologies, it is noticeable that
they are similar. Safety is the leading
acceptance factor for autonomous
driving – which is in line with the findings
for UAM. Probably because both
solutions carry goods and passengers.
Privacy on the other hand is also a top
concern in the smart home space.
1. Share of the 173 mentions regarding challenges (multiple per publication possible);
Infrastructure
Regulations
3.5
Safety
Air Traffic Management
Noise
6.9Environmental impact
Security
Certification
CommunicationsCost
7.5
EconomicsSocietal acceptance
8.7
Technology
Legal framework
OperationsPublic perception
11.0
8.78.1
4.0
2.3
4.0
3.54.0
3.5
2.92.3
2.32.3
Public acceptance
Generic term used in literature – may include noise, visual pollution, air pollution, land use, protection of species & biotopes, protection of water & soil, climate, and natural resources.
Challenges for UAM1
%
Figure 5: Challenges for UAM
171.1 Literature review
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Comparison with societal acceptance factors for smart home and autonomous driving
As explained at the beginning of this section, literature for adjacent technologies, such as smart home and autonomous driving, was also reviewed for societal acceptance factors. The literature reviewed is indicated in the Bibliography and the findings are displayed in Figure 7.
Safety was a leading acceptance factor for autonomous driving. This is in line with the findings for UAM and could be explained by both topics being mobility solutions carrying goods or passengers. Noise, on the other hand, does not appear as a major topic for autonomous driving, as a level of noise comparable to current passenger cars seems to be acceptable.
Privacy is also a top-ranking concern in the smart home space, which potentially explains the lower usage rate for this technology in Western Europe compared to the United States.
In the reviewed surveys for the acceptance of autonomous driving, the survey participant’s openness to and interest in new technologies has a stronger influence than their sociodemographic background, such as age, gender, or employment status. With both autonomous vehicles and smart homes, participants with a positive attitude towards the technology were more likely to use it.
1817
655
4333
222222222
Security
Visual annoyance
Noise
Increased travel optionsEnvironmental impact
SafetyPrivacy
Ethics
Benefits for self or community
Data concernsAffinity to automation
Perceived usefulnessTrustWillingness to share (ride)
Job loss concernsLack of experienceMisusePrice
1. Share of the 188 mentions regarding societal acceptance (multiple per publication possible)
Societal acceptance factors1
Generic term used in literature and may include noise, visual pollution, air pollution, land use, protection of species & biotopes, protection of water & soil, climate, and natural resources.
%
Figure 6: Societal acceptance factors
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Figure 7: Societal acceptance barriers for smart homes and autonomous driving
PrivacySmart Home penetration rate in West Europe 15% lower in comparison to the USA. Major adoption barriers in the EU are privacy, interoperability, possibility to control devices in local languages1.
2 EU countries (Germany and Belgium) have an option on Google maps for citizens to pixelate the houses. The option was introduced because of the high population privacy concerns in these countries2
SafetySafety was mentioned by 4 from 6 publications as a leading acceptance factor for autonomous vehicles. According to literature, people have great concerns about AV safety because the technology is not mature enough and the public does not have enough knowledge about it3.
Source: 1 Strategy Analytics - 2019 Smart Home Forecast, 2 Googlemaps analysis, 3 Literature research for autonomous driving+
NoiseNo major concerns about noise of cars or busses in the EU were identified. The level of noise comparable to an average passenger car seems to be acceptable
by the population3.
Additional insights3
Self-reported acceptance of driverless vehicles is more strongly determined by domain-specific attitudes than by sociodemographic characteristics.
People in Europe and Asia have substantial differences in attitudes toward AVs. Safety is one of the most concerned factors of AVs by respondents.
Risk perception is identified as a major inhibitor to the use intention of smart homes.
The use of smart home technology is influenced by positive attitude (perceived newness, societal influence, innovativeness) towards it.
1919
PrivacySmart Home penetration rate in West Europe 15% lower in comparison to the USA. Major adoption barriers in the EU are privacy, interoperability, possibility to control devices in local languages1.2 EU countries (Germany and Belgium) have an option on Google maps for citizens to pixelate the houses. The option was introduced because of the high population privacy concerns in these countries2
SafetySafety was mentioned by 4 from 6 publications as a leading acceptance factor for autonomous vehicles. According to literature, people have great concerns about AV safety because the technology is not mature enough and the public does not have enough knowledge about it3.
NoiseNo major concerns about noise of cars or busses in the EU were identified. The level of noise comparable to an average passenger car seems to be acceptable by the population3.
Source: 1 Strategy Analytics - 2019 Smart Home Forecast,2 Googlemaps analysis, 3 Literature research for autonomous driving+
Additional insights3
Self-reported acceptance of driverless vehicles is more strongly determined by domain-specific attitudes than by sociodemographic characteristics.People in Europe and Asia have substantial differences in attitudes toward AVs. Safety is one of the most concerned factors of AVs by respondents. Risk perception is identified as a major inhibitor to the use intention of smart homes.The use of smart home technology is influenced by positive attitude (perceived newness, societal influence, innovativeness) towards it.
PrivacySmart Home penetration rate in West Europe 15% lower in comparison to the USA. Major adoption barriers in the EU are privacy, interoperability, possibility to control devices in local languages1.2 EU countries (Germany and Belgium) have an option on Google maps for citizens to pixelate the houses. The option was introduced because of the high population privacy concerns in these countries2
SafetySafety was mentioned by 4 from 6 publications as a leading acceptance factor for autonomous vehicles. According to literature, people have great concerns about AV safety because the technology is not mature enough and the public does not have enough knowledge about it3.
NoiseNo major concerns about noise of cars or busses in the EU were identified. The level of noise comparable to an average passenger car seems to be acceptable by the population3.
Source: 1 Strategy Analytics - 2019 Smart Home Forecast,2 Googlemaps analysis, 3 Literature research for autonomous driving+
Additional insights3
Self-reported acceptance of driverless vehicles is more strongly determined by domain-specific attitudes than by sociodemographic characteristics.People in Europe and Asia have substantial differences in attitudes toward AVs. Safety is one of the most concerned factors of AVs by respondents. Risk perception is identified as a major inhibitor to the use intention of smart homes.The use of smart home technology is influenced by positive attitude (perceived newness, societal influence, innovativeness) towards it.
PrivacySmart Home penetration rate in West Europe 15% lower in comparison to the USA. Major adoption barriers in the EU are privacy, interoperability, possibility to control devices in local languages1.2 EU countries (Germany and Belgium) have an option on Google maps for citizens to pixelate the houses. The option was introduced because of the high population privacy concerns in these countries2
SafetySafety was mentioned by 4 from 6 publications as a leading acceptance factor for autonomous vehicles. According to literature, people have great concerns about AV safety because the technology is not mature enough and the public does not have enough knowledge about it3.
NoiseNo major concerns about noise of cars or busses in the EU were identified. The level of noise comparable to an average passenger car seems to be acceptable by the population3.
Source: 1 Strategy Analytics - 2019 Smart Home Forecast,2 Googlemaps analysis, 3 Literature research for autonomous driving+
Additional insights3
Self-reported acceptance of driverless vehicles is more strongly determined by domain-specific attitudes than by sociodemographic characteristics.People in Europe and Asia have substantial differences in attitudes toward AVs. Safety is one of the most concerned factors of AVs by respondents. Risk perception is identified as a major inhibitor to the use intention of smart homes.The use of smart home technology is influenced by positive attitude (perceived newness, societal influence, innovativeness) towards it.
PrivacySmart Home penetration rate in West Europe 15% lower in comparison to the USA. Major adoption barriers in the EU are privacy, interoperability, possibility to control devices in local languages1.2 EU countries (Germany and Belgium) have an option on Google maps for citizens to pixelate the houses. The option was introduced because of the high population privacy concerns in these countries2
SafetySafety was mentioned by 4 from 6 publications as a leading acceptance factor for autonomous vehicles. According to literature, people have great concerns about AV safety because the technology is not mature enough and the public does not have enough knowledge about it3.
NoiseNo major concerns about noise of cars or busses in the EU were identified. The level of noise comparable to an average passenger car seems to be acceptable by the population3.
Source: 1 Strategy Analytics - 2019 Smart Home Forecast,2 Googlemaps analysis, 3 Literature research for autonomous driving+
Additional insights3
Self-reported acceptance of driverless vehicles is more strongly determined by domain-specific attitudes than by sociodemographic characteristics.People in Europe and Asia have substantial differences in attitudes toward AVs. Safety is one of the most concerned factors of AVs by respondents. Risk perception is identified as a major inhibitor to the use intention of smart homes.The use of smart home technology is influenced by positive attitude (perceived newness, societal influence, innovativeness) towards it.
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1.2. Industry status and projection
This section of the report provides an overview of the industry status, including UAM aircraft types, use case applications for these UAM aircraft and the UAM stakeholder environment.
Overview
As of 2021, the UAM market is still in an early stage, while showing increasing momentum. Many start-ups and companies are emerging across the entire value chain. In particular, the eVTOL manufacturing and Original Equipment Manufacturer (OEM) sector is rapidly evolving. More than 200 eVTOL designs and concepts are currently being investigated and developed with many prominent ones like Volocopter, Joby, Lilium, Airbus, or Kitty Hawk. Some of these air vehicle systems are already in advanced certification stages. Europe is leading with many OEMs such as Volocopter, Airbus, Lilium, Ascendance, and Pipistrel in advanced certification stages and a significant number of pilot regions and projects, for example in Frankfurt, Paris, Cologne and Dusseldorf, Linz, Helsinki, and Ingolstadt (see Figure 8 and Figure 9).
Figure 8: Passenger vehicle certification announcements (non-exhaustive)
Expected certification, i.e., commercial rollout possible
Frankfurt, Paris in 2021 (test flights)
N. a.
N. a.
Cologne and Dusseldorf in 2025Linz in 2021 (test flights), Seville, and Llíria
N. a.
N. a.
N. a.
N. a.
N. a.
Paris
Announcements made for EU
N. a.
N. a.
N. a.
N. a.
N. a.
Public test flights in Stuttgart Helsinki and Singapore
No certification for current model in Western countries
Currently manned test flights in New Zealand
Test flight certification
Design stage 2024
European First manned flight
2016 2017 20192018 2020 2021 2022 2023 2024+
Design stage2026
Design stage
Design stage
2024
Design stage
Design stage
2024
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Figure 9: Cargo vehicle announcements: (non-exhaustive)
Expected certification, i.e., commercial rollout possible
Announcements made for EU
European First manned flight
2016 2017 20192018 2020 2021 2022 2023 2024+
Part 135 certified for in the US Helsinki in 2020
Ingolstadt in 2022
N. a.
Testing in France and Austria
N. a.
N. a.
Design stage
N. a.
Design stage
UAM vehicle types
In general, UAM aircraft layouts for vertical take-off and landing (VTOL) can be categorised into three archetypes:
i. Vectored thrust
The same propulsion units first provide lift during the hover and then swivel to create thrust in the cruise phase. During the cruise phase, lift is generated by the wings. This layout is better suited to longer-distance flights, as the system is more efficient but more complex than the other concepts. An example can be seen on the left side of Figure 10.
ii. Lift + cruise
This layout has separate propulsion units for the hover and cruise phases. Wings create the necessary lift during the cruise phase. Lift + cruise is suited to shorter distance flights than vectored thrust, but to longer distances than wingless. It is potentially easier to certify than vectored thrust because the propulsion systems are separate. An example can be seen in the middle of Figure 10.
iii. Wingless (multicopter)
Here the propulsion units are fixed in position and create lift all the time. This is the option that offers the shortest flight distances and is overall the simplest concept, as it is avoiding any unnecessary movable parts (e.g. thrust vectoring). An example can be seen on the right side of Figure 10.
1.2.1 Aircraft and Use Cases
In the following subchapters, the aircraft types and certification timelines of the main use case categories of this study (passenger transport, cargo transport, and emergency) are discussed. Detailed statements related to autonomy levels,
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range, energy consumption and required ground infrastructure are not presented in the following as these are kept confidential by the relevant actors.
Passenger transport aircraft
The commercial transport of people by UAM aircraft is covered by this segment. This can be, for example, a flight between a city centre and an airport, flights within a metropolitan area, or flights within a city for sightseeing.
For the passenger transport use cases, vectored thrust (i) appears to be a preferred solution, with 7 out of 16 of the concepts reviewed opting for this solution (e.g. Bell, Hyundai and Joby). This is followed by lift + cruise (5 out of 16, e.g. EVE, BETA and Wisk), and finally wingless (4 out of 16, e.g. Airbus, Volocopter and EHang). Planned passenger numbers range from one to five.
Figure 10: UAM vehicle types
For passenger transport use cases, (i) vectored thrust
appears to be the preferred solution for OEMs. Most
of them plan to start operations with a pilot on board.
First certifications for passenger transport use cases
are estimated for 2022. To ensure infrastructure
needed, vehicle manufacturers, cities and infrastructure
companies are cooperating. Most of this is happening in
Europe right now.
For cargo transport use cases, (ii) lift + cruise is the
preferred archetype for OEMs, followed by wingless.
Most concepts plan to fly autonomously from the
beginning and have a payload between 0.7 to 200 kg.
First operational certifications are already achieved.
For emergency use cases, (iii) wingless vehicles are
preferred, all planned to be remote controlled. They can
cover transport of medical emergency personnel to an
accident site, patients to a hospital but also e.g. direct
firefighting.
Source: Hyundai, Wisk, Volocopter, evtol.news, NASA.gov
Hyundai SA1 eVTOL Wisk (Kitty Hawk) Cora Volocopter 2X
Thrusters used for lift and cruise
Independent thrusters used for cruise as for lift
Thrusters only for lift, cruise via rotor pitch
Vectored Thrust Lift + Cruise Wingless (Multicopter)
Benefits Optimized for both hover and cruise
Lift provided by wings for cruise for highest efficiency
Highest cruising speeds
Redundancy benefits of multicopter without collective or cyclic actuation
Wing configuration allows for more speed in cruise
High redundancy and simple controls
Significantly quieter than helicopters
Lower maintenance and lightweight
Implications Greater mechanical complexity
Suboptimal for both hover or cruise
Slowest cruising speeds/least efficient
Example
Passenger, cargo and emergency use cases and potential vehicles
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Most OEMs plan to start operations with vehicles with a pilot on board (e.g. Volocopter, Lilium and Bell). Very few plan to start operations with fully remote controlled or autonomous vehicles (e.g. EHang and Wisk). All concepts are powered by a battery electric propulsion system, except for those from Moog and Ascendance Flight Technologies, which utilise a hybrid electric propulsion system.
The earliest estimated certification year for the companies reviewed within the passenger transport use case is 2022 (for Volocopter), followed in 2023 by Airbus and Joby. The bulk of players (e.g. Lilium, EHang, Wisk etc.) announced they would expect certification in 2024 or later. The most ambitious timelines were four years from the start of the design phase to planned certification for Vertical Aerospace and Ascendance Flight Technologies. Both companies are currently in the design phase. The European OEMs Lilium and Volocopter were among the first to start development of passenger transport aircraft.
UAM ground infrastructure
Dedicated infrastructure is required for the initial operation of UAM passenger transport. ‘Vertiports’ will probably appear in different sizes and numbers in different cities, depending on expected traffic volumes. The largest vertiports will be the fewest in number in a city, and the smallest ones will be the most numerous. Figure 11 indicates potential numbers for different city archetypes in mature UAM network state. The number of landing pads is different for the three vertiport types, with vertipads only having one or two, while a vertihub can have around ten landing pads. The number of landing pads per vertiport multiplied by the respective number of vertiports in a city results in the total landing pad number.
Source: Center for Future Air Mobility
Medium citiesLarge cities
Medium, less dense, medium income, urban/suburban city, Sevilla, Lisbon, Dusseldorf, Riga, Athens
Large, dense, high-income urban city, e.g., Paris, Berlin, Madrid, Hamburg, Vienna, Barcelona
20-45Total landing pads40-60
Major suburban commuting stations, private use for high net worth individuals, or in wealthy suburbs
3-5Outposts, areas of interest or private use
Vertipads3-5
Major corporate headquarters, major retail districts, and major commuting stations
3-7Near concentrations of high origin and destination points
Vertibases5-10
Main airport, downtown, and major work district
1-2Major airports, city centres, and major commute corridors
Vertihubs2-3
Figure 11: Urban Air Mobility infrastructure may come in scalable size types
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Two important factors for locating vertiports will be the ease of access to them, as well as the electricity infrastructure connection. As the energy for the operations of most UAM aircraft will be electricity stored in batteries, the recharging of the batteries will probably happen at the vertiports and therefore a suitable connection to the electricity grid will probably be required.
At the moment, the development of vertiports seems to be mostly through collaborations between experienced infrastructure players and UAM aircraft manufacturers, although manufacturers, have also demonstrated development of some of their own concepts. Some infrastructure players have also demonstrated concepts they are developing on their own that would be compatible with various
UAM aircraft manufacturers. But at the moment the interoperability of these concepts is difficult to assess.
Europe seems to be a leading market for passenger transport, as demonstrated by the announcements of collaborations between UAM aircraft manufacturers, cities, and infrastructure companies by the various regions, as can be seen in Figure 12.
Cargo transport aircraft
This segment covers the transport of goods by UAM aircraft for commercial or industrial applications. This can include, for example, last-mile delivery, delivery to a hub, or rural delivery of supplies. The transport of emergency and medical goods, such as organs and blood, is excluded from this category as it is covered by the emergency use case.
Source: Center for Future Mobility
North America AsiaEurope
AustraliaUSA
OEMs without location OEMs without location
Collaboration
Potentially open to all OEMs
OEMs without location
France
China
Singapore
Concepts for Joby (formerly UBER)Germany
Italy
Figure 12: Vertiport announcements
What are vertiports?Vertiports are needed to enable take-off and landing of air taxis. They are expected to appear in different sizes. Depending on the traffic of a city, number of vertiports will vary. At the moment, the development of vertiports seems to be mostly through collaborations between experienced infra- structure players and UAM vehicle manu-factures.
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The delivery could be lowered via a winch on the UAM vehicle into the garden of the receiving person or organisation, or the vehicle could land on the roof of a multistorey building and the delivery could be picked up from there. Another option is the delivery to a fixed station in the vicinity of the receiving person, similar to the self-service parcel terminals already used today.
The lift+cruise aircraft is the preferred archetype in this category (with four out of eight OEMs using this concept), followed by wingless (three out of eight) and vectored thrust (one out of eight). The stated payload of the concepts ranges from 0.7 to 200.0 kg. Only two concepts will initially be remote controlled (EHang and Volodrone); the others are already planned to be autonomous during initial operation. Six of the concepts use battery electric propulsion, while two will use hybrid propulsion, which includes two or more sources of propulsion in one design (Pipistrel and AutoFlight).
Of the companies reviewed within the cargo use case, Wing and Amazon have already achieved operational certification according to Part 135. From an aircraft point of view, Quantum-Systems is aiming for certification in 2022, while Volodrone and Pipistrel are aiming for 2023. The other players did not state a definite target for aircraft certification and are mostly in the prototype stage.
From a European OEM point of view, quantum systems had already started development of a cargo vehicle with 0.7 kg payload in 2017, while Volocopter and Pipistrel announced plans for vehicles with a larger payload (200 and 460 kg respectively) in 2019 and 2020.
Emergency aircraft
Aircraft for emergency-related use cases are summarised in this segment. These can cover applications such as the transport of medical emergency personnel to an accident site, the transport of patients to a hospital, the evaluation of emergency areas, direct firefighting, or the delivery of medical and emergency supplies.
The emergency UAM aircraft development does not seem to be a focus for European OEMs so far. Only Volocopter collaborates with ADAC Luftrettung, a German non-profit air medical provider, on the use of Volocopter’s passenger UAM for flying emergency doctors to accident sites. However, any passenger transport UAM could in principle be used for transport of a doctor, while for a patient transport a dedicated cabin modification would be needed. Thus, aeromedical services are more dependent on the operations regulations.
Other aircraft reviewed in this category are all wingless since non-urban applications such as those by Zipline are out of scope. Airobotics, DJI, IAI and Parrot plan to use vehicles for the visual assessment of emergency locations, while EHang plans to use them for extinguishing fires in high-rise buildings. All concepts reviewed were remote controlled and will have an electric propulsion system either powered by batteries or via a tether, for IAI.
No certification timelines were found for the five manufacturers reviewed (Airobotics, DJI, EHang, IAI and Parrot) in this segment.
-
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1.2.2 UAM stakeholder environment
The emerging UAM industry and ecosystem is influenced by many stakeholders. The main UAM stakeholders can be categorised into four groups along the value chain, as can be seen in Figure 13.
Figure 13: UAM vehicle types
Governments, public institutions & regulatorsSupranational & national: EU institutions and bodies, EASA, air traffic control organizations, EU member state governments, state authorities, military & police
Local: Local authorities, municipalities, city officials, urban and city planners, public institutions and organizations
Major influence on societal acceptance
Indirectly affected third partiesPrivate individuals: Residents, communities, real-estate owners, citizens
Professionals: Pilots, academia, innovators
Associations: Local environmental protection associations, local traveller's’ associations, unions, lobbies, associations, environmental groups
Extended industry: Airports, aerospace & automotive industry, energy providers, public transport providers, insurance providers, ticket brokers, businesses in other industries potentially interested in entering UAM space
UAM industryManufacturers, UAM operators, maintenance services, airport operators, service providers, vertiports, communication providers, suppliers
Potential usersUrban residents, travellers, commuters, high wealth individuals, car users, emergency services, public transport users
UAM industry
Potential users
Governments,public
institutions ®ulators
Indirectly affected third
parties
The following section covers the different stakeholder groups in more detail and lists their motivation, expectations, and concerns.
The UAM industry stakeholder group includes all entities directly involved in the development, manufacturing, operation, and servicing of UAM aircraft and services. The main motivation for this group is generating a profit from their activities. They may also be motivated by advancing technologies, keeping or increasing their number of employees, or being a first mover.
While working on UAM topics, this group may hope for a stable regulatory framework, minimal levels of bureaucracy, support for building up a new industry, access to a qualified workforce, and beneficial taxation. Their main concerns could be the impact of regulation on the economics of UAM, excessive regulation, public opinion, nimbyism, and environmental issues.
For the potential user stakeholder group, time and cost savings, as well as comfort, are some of the main decision criteria for selecting a mode of transport. The expectations of the potential user group for UAM will probably be safety,
-Stakeholders at all level are important for societal acceptance of UAM
Urban Air Mobility needs to meet expectations of a wide variety of stakeholders. This involves reconciling different social acceptance perspectives.
271.2 Industry status and projection
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reliability, predictability, affordability, ease of use, and convenience. Topics they may have concerns about are noise, safety, environmental impact, benefit for self and/or the community, automation, and accessibility.
From the governments, public institutions, and regulators group viewpoint, three different levels of political structures come into play: supranational, national, and local. The focus for this stakeholder group is the public good, safety of the public, an efficient mobility system, limitation of congestion and pollution, the creation of jobs, supporting and building up an industry in their respective jurisdictions, the environment, and public opinion.
The expectations regarding UAM are probably that it should generate a positive contribution to the community, contribute income tax that finances governmental tasks, and that the industry complies with regulations. The main concerns are likely around public opinion, loss of life, impact on voters, prestige for their respective jurisdictions, under- or over-regulation, and environmental issues.
Members of the indirectly-affected third-parties group may be impacted by an evolving UAM industry. They can be further divided into private individuals, professionals, associations, extended industry, and potential competitors. UAM will most likely be evaluated by this stakeholder group through the lens of the benefit for oneself and/or for society. Opportunities for growth and development are the probable expectations from this group and becoming irrelevant or losing job security may be some of their concerns.
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291.2 Industry status and projection 29
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1.3 UAM high level societal benefits and risks
The introduction of new technologies comes with benefits and risks for the users, but also for the general public. The following section reflects the high-level societal benefits and risks initially identified through the research and literature review. These elements were then used to build the survey questionnaires and were compared ultimately with the results of the survey (see Conclusion).
1.3.1 Benefits based on market models, literature and expert interviews
UAM will have societal benefits for the EU and Europe. These benefits come in a variety of dimensions, as shown in Figure 14.
The estimated market size of UAM in Europe, including R&D, vehicle manufacturing, operations and infrastructure construction, will be approximately EUR 4.2 billion in 20303, which represents almost one third of the global market and hints at the opportunity that this industry may offer for Europe. The estimated market size may create or sustain approximately 90,000 jobs in 2030, based on labour spending for constructing related infrastructure and operating the UAM. Manufacturing jobs are not included, as the whole supply chain setup is still uncertain. If we visualise what this market size would mean for the Paris metropolitan area in terms of UAM aircraft, the estimates range from approximately 3,000 to 3,500 UAM aircraft for passenger and cargo transport in 2030. In this estimate, UAM passenger aircraft represent the smallest part with numbers between 160 and 180, whereas the estimates for the UAM cargo aircraft and delivery drones range from 2,840 to 3,300.
Local emissions by UAM, in the city environment, could be almost zero if battery electric propulsion systems are used. Most of the reviewed UAM concepts already rely upon this propulsion type, with a minority working on hybrid electric propulsion systems.
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3. Source: McKinsey Center for Future Mobility UAM Market Model
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1. Based on McKinsey VTOL market model2. Assuming same safety level as commercial air transport in the EU3. Based on direct, indirect and induced jobs created by CAPEX and OPEX spend
of UAM industry in Europe in 20304. Compared to a helicopter with conventional kerosene propulsion5. Compared to a car drive on a Thursday at 5pm
Source: VTOL team, Eurostat, Google Maps
~4.2 bn €market size in Europe in 20301
15-40 minsaved in average on travel time by UAM for a city to airport transfer5
100%reduction of local emissions for electric propulsion4
~90 000jobs created in the Europe in 20303
1 500 timesless likely to be involved in a fatal accident compared to road transport on a passenger kilometre basis2
~73%faster delivery of organs between city hospitals possible5
~31%of global UAM market to be located in Europe in 20301
Figure 14: UAM benefits for the EU and Europe
One of the major benefits of UAM for users will be time savings. For example, a city-to-airport transfer in Paris by air taxi could 2 to 4 times faster compared to a car journey on a Thursday evening during rush hour. Also, medical transportation of equipment or organs could be performed approximately 73 percent faster by drone than by ambulance, taking the example of a trip in Berlin on a Thursday evening, during rush hour.
If UAM passenger transport achieves the same level of safety as aviation did within the EU in 2018 (0.01 fatalities per billion passenger kilometers), it would then be approximately 1,500 times safer on a passenger-kilometer basis than road transportation. This number is based on data for road transport and commercial air transport in the EU. As a first step the fatalities per million passenger kilometers for both modes of transport were calculated and in a second step these respective numbers were put in proportion.
1. Based on McKinsey VTOL market model2. Assuming same safety level as commercial air transport in the EU3. Based on direct, indirect and induced jobs created by CAPEX and OPEX spend
of UAM industry in Europe in 20304. Compared to a helicopter with conventional kerosene propulsion5. Compared to a car drive on a Thursday at 5pm
Source: VTOL team, Eurostat, Google Maps
~4.2 bn €market size in Europe in 20301
15-40 minsaved in average on travel time by UAM for a city to airport transfer5
100%reduction of local emissions for electric propulsion4
~90 000jobs created in the Europe in 20303
1 500 timesless likely to be involved in a fatal accident compared to road transport on a passenger kilometre basis2
~73%faster delivery of organs between city hospitals possible5
~31%of global UAM market to be located in Europe in 20301
1. Based on McKinsey VTOL market model2. Assuming same safety level as commercial air transport in the EU3. Based on direct, indirect and induced jobs created by CAPEX and OPEX spend
of UAM industry in Europe in 20304. Compared to a helicopter with conventional kerosene propulsion5. Compared to a car drive on a Thursday at 5pm
Source: VTOL team, Eurostat, Google Maps
~4.2 bn €market size in Europe in 20301
15-40 minsaved in average on travel time by UAM for a city to airport transfer5
100%reduction of local emissions for electric propulsion4
~90 000jobs created in the Europe in 20303
1 500 timesless likely to be involved in a fatal accident compared to road transport on a passenger kilometre basis2
~73%faster delivery of organs between city hospitals possible5
~31%of global UAM market to be located in Europe in 20301
311.3 UAM high level societal benefits and risks
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1.3.2 Risks and acceptance based on literature and expert interviews
There are also a few risks associated with the implementation of UAM in Europe (see Figure 15). Amongst the top concerns in the literature or stated by experts are:
• Noise: is perceived as a prevalent risk of UAM. This includes the noise generated by the vehicles when they take-off and land, as well as while they are in flight.
• Safety: Ranks high among the risks of UAM mentioned in the reviewed literature, as an unsafe system could have widespread implications for public acceptance.
• Privacy: Society may also be concerned about privacy, as UAM aircraft like air taxis and drones may fly above or close to places of residence.
• Visual pollution: Was mentioned as a potential nuisance, which may hamper public acceptance of UAM and is therefore a risk to its widespread rollout.
• Job losses: Some jobs may become obsolete due to the introduction of UAM, and this could lead to resentment against it. Affected industries could include logistics and taxi services.
• Environmental issues: The environmental impact of UAM may be almost zero on a local emissions level for battery electric vehicles, but the required electricity still has to be generated and the vehicle components have to be manufactured, assembled and eventually disposed of. Focus should be placed on reducing the overall environmental impact of UAM aircraft during the design phase.
• Affordability: Another risk for UAM is the affordability of the services for a large part of society. If the services are only available to more affluent individuals but the disadvantages (like noise) are borne by everyone, this could hamper the acceptance of UAM within society.
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Figure 15: UAM risks for the EU and Europe
Source: Bibliography ID 20, 26, 52, 53, 61, 87
Noise"..there are certain threats that could
impede the sustainable and thus successful introduction of UAM to our
cities, with noise being a prominent limitation." (26)
Privacy"Civil liberties groups have privacy
concerns with widespread UAM adoption..." (20)
Visual pollution"The sensitive topic of visual and noise pollution must also be addressed." (52)
Environmental impact"Air pollution caused by pollutants such as particulate matter, nitrogen oxides and ozone, as well as odour nuisance should be avoided." (53)
Obsolete jobs"There is concern that autonomous
technology will render jobs obsolete across multiple industries" (20)
Affordability"Public acceptance of these new systems
and services is imperative, driven by ... ...and affordability." (61)
Safety"The key areas of discussion to move
forward will be to meet, or exceed, the current safety parameters with these
new vehicles." (87)
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An extensive market analysis was performed to identify a list of EU cities where the deployment of local UAM markets appears plausible in the years to come, due to the local conditions and needs. A further objective was to identify six cities from this list where the quantitative survey could be conducted. As respondents to the quantitative survey needed to include sufficient representatives of the cross-sections of the local population, only large cities with a minimum number of inhabitants (300,000 for cities and 2,000,000 for cross-border regions) were pre-selected. This list is only indicative and not exhaustive, and the absence of a city does not imply that UAM would not work well in that location.
Since the selection process was very comprehensive, only an overview and its methodology is given below, together with the overall results. Further details can be found in the Appendix.
2. Assessment of urban European target markets
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2.1 Use case prioritisation
The review of international literature identified six categories of principal use cases for UAM deployment (see Figure 16): Transportation (passenger transfer for commercial applications), delivery (transport of goods for commercial and industrial applications), emergency services (response in case of an accident, fire, disaster etc.), civil surveillance and other operations (manual operations that physically interact with the environment), sovereign functions (surveillance and analytics of areas, objects or people), and emitting (providing multimedia bandwidth by emitting signal/video/sound).
For each of these use case categories, societal risks and benefits were evaluated to identify those with the highest risks and benefits, and a framework was created to break down benefits and risks into categories. This allowed us to understand which use cases are likely to be deployed in the EU in the next five to ten years and to include them in the survey.
36 A study on the societal acceptance of Urban Air Mobility in Europe
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Low – Minimal or no benefits for the majority of use cases
High – Significant benefits for the majority of use cases
Societal benefits
Societal risks
Low – Minimal or no new risks
Sovereign functions(Surveillance and analytics of areas, objects or people)
Transportation(Passenger transfer for commercial applications)
Delivery(Transport of goods for commercial and industrial applications)Operate
(Manual operations that physically interact with the environment)
Emitting(Providing multimedia bandwidth by emitting signal/video/sound)
6
Emergency services(Response in case of an accident, fire, disaster, etc.)
High – Implies major risks transporting people, or flying over people
1 2
3
4
5
Figure 16: Transformation, emergency services and delivery are use cases with high risks and high benefits
The results indicate that three use cases have the highest risk and benefits, and are therefore very important candidates for societal acceptance analysis: (1) commercial passenger transport by electric vertical take-off and landing (eVTOL) with a pilot onboard, (2) emergency services use cases (both medical equipment by drone and people transport by eVTOL with a pilot onboard), and (3) delivery transport by drones for commercial and industrial applications. These results also support the request from the European Union Aviation Safety Agency (EASA) to include only people transport, goods delivery, and emergency services in the scope of the UAM target market analysis.
These three main use cases were analysed in detail and sub-use-cases were defined for each. The analysis centered on how often sub-use-cases were mentioned in the literature review as well as during interviews with external and internal UAM experts. For each sub-use-case, this analysis indicated whether it was a likely candidate for initial implementation in the EU, its near-term viability for 2025 to 2030, and which benefits and risks it involved. Based on this evaluation, six priority sub-use-cases were chosen for further analysis.
372.1 Use case prioritsation
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1. Passenger transfer use cases for commercial application
The sub-use-cases for commercial passenger transport were divided into three categories (see Figure 17 below): intracity transport (under 40 km), suburb/region-to-city transport (under 100 km) and regional city-to-city transport (between 100 km and 300 km). Airport shuttle (A), sightseeing (C) and fixed metropolitan network (D) were identified as the sub-use-cases with the highest benefits, lowest risks, and the highest viability for the initial UAM introduction in 2025 to 2030. They have therefore been chosen for the survey city-selection process (for more details see Appendix). As can be seen in chapter 1.2 (UAM vehicle types), these operations will be piloted in the first years of introduction.
Figure 17: Airport shuttle, sight-seeing (loop) and fixed metropolitan network are most important passenger use cases for the survey
2.Su
burb
/ reg
ion-
to-c
ity
(<10
0km
) 3.
Reg
iona
l cit
y -to
-cit
y (1
00-3
00km
)
B Fixed urban network
Car, taxi, subway, bus
Aerial taxi for faster travelling within dense urban area
C Sight-seeing (loop)
Bus, taxi, walking
Pre-defined trip over iconic sights (e.g., Eiffel Tower)
EFlexible metropolitanpoint to point transfer
Car, ridesharing, subway, bus, walking, biking
Flexible routes, e.g., to commute from residential rural suburb to office in a city centre
DFixed metropolitan network
Train, public transport
Flights connections within a metropolitan over slow or often congested routes
1. In
trac
ity
tran
spor
t (<
40km
)
A Airport shuttle
Car, taxi, subway, bus, walking, biking
Ride from airport to vertiport located in city centre (e.g., main rail station)
F Fixed regional network
Driving, ridesharing, train, subway, bus
Ferrying over slow or often congested routes within regional span
GFlexible regional point to point transfer
Helicopter, boat
Flexible intercity network within regional span
High Medium LowIn scope of city selection
Category Risks Benefits
Frequency of publications and indications from
experts interviews2025
viability AlternativesDescriptionUse case
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Figure 18: First aid and medical supplies are most important emergency use cases for the survey
Category Risks Benefits
Frequency of publications and indications from
experts interviews2025
viability AlternativesDescription
3. D
isas
ter m
anag
emen
t2.
Res
pons
e in
cas
e of
an
acci
dent
J
K
Fire-fighting team
Firefighting with firefighting foams and extinguisher bombs for high-rise buildings
L
HelicopterRescue of people from disaster/emergency areas
M
4.Su
pplie
s de
liver
y HelicopterDelivery of time-critical medical supplies (blood, organs) to hospitals
O
HelicopterDropping of lifebuoys or emergency supplies to catastrophe regions
P
I
Ambulance, car, helicopter
H First aid Transport of medical emergency forces to site of accident, e.g., on highways
Use case
Ambulance, helicopter, car
Transfer of injured or sick patient to closest hospital
Patient transport
Helicopter, Aircraft
Drones with cameras providing first responders a real-time elevated view of the situation, directing emergency teams on the ground
Evaluation of a disaster/emergency area
Fire-fighting team
Observation of a fire incident site drones with thermal and visual cameras, enabling rapid and informed decisions for firemen
Observation of a fire incident site
Fire-fighting
Emergency rescue
Medical supplies
Emergency supplies
High Medium LowIn scope of city selection
2.Emergency service use cases
The sub-use-cases for emergency services were divided into three categories: accident response, disaster management and supplies delivery (see Figure 18 below).
First-aid and medical-supply delivery sub-use-cases were identified as the sub-use-cases with highest benefits, lowest risks, and viability for initial UAM introduction in 2025. Therefore these have been chosen for the survey city-selection process (for more details see appendix). For our purposes, first-aid (H) includes transport of medical emergency forces to the site of an accident and these operations are expected to be piloted in the first years, as in the case of passenger transport. Medical supply delivery (O) refers to the transport of blood or organs to an hospital (for example) and can support even faster delivery than existing modes of transport (see section “UAM vehicle types” for more information).
392.1 Use case prioritsation
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3. Goods delivery for commercial and industrial applications
The sub-use-cases for goods delivery were divided into two categories: last-mile delivery (e.g. the last section of the supply chain, which delivers goods to their final destination) and long-distance delivery (see Figure 19 below).
The last-mile delivery category was identified as the priority sub-use-case having the highest benefits, lowest risk, and best viability for the initial UAM introduction in 2025 to 2030 and has therefore been chosen for the survey city selection process.
Figure 19: Last mile delivery holds the most important sub-use-case for the survey 2.2 Target market identification
6. L
ong
dist
ance
del
iver
y
U Heavy cargo
Ship, boat, helicopter
Forwarding of containers or bulk goods over a route with little infrastructure
S Delivery to a hub
Truck, carGoods delivery (e.g. retail goods) in urban area to a hub along a pre-defined route
V Industrial applications
TruckTransport of instantaneously needed goods for industrial applications, e.g., spare parts
W Rural delivery of supplies
TruckGrocery delivery to home in area with long travelling distance to next shop
Car, bike, motorbike
R Deliver to a private property (e.g. garden)
Fast delivery (e.g. food or last minute gift) in urban area to private residences
5. L
ast m
ile1
deliv
ery
High Medium LowIn scope of city selection
Category Risks Benefits
Frequency of publications and indications from
experts interviews2025
viability AlternativesDescriptionUse case
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2.2 Target market identification
For each of the six prioritised sub-use-cases (airport shuttle, sightseeing, fixed metropolitan network, first aid, medical supply delivery and last-mile delivery), the project identified the most attractive EU urban target markets – from a business perspective – for UAM OEMs and UAM operators. These target markets are likely to see the first deployments of commercial UAM services in the EU and were therefore deemed to be important candidates for societal acceptance analysis.
To identify target markets for each use case, a four-step methodology was established:1. European cities were pre-selected based on metrics relevant to the specific use case;2. Pre-selected cities were ranked based on defined KPIs: the 15 cities with the highest ranking were selected for the
next step;3. An infrastructure assessment, relevant to the specific use case, was performed for these 15 cities;4. A timeline assessment (number of announced pilot projects, timelines of known pilot projects, implementation
feasibility by 2025) was performed for these 15 cities – this information was used for informative purposes only and not for further selection.
Figure 20 shows the city selection process on the airport shuttle use case, as an example.
Use cases most likely to be deployed first (in the EU)• airport shuttle• sightseeing• fixed metropolitan network • first aid • medical supplies • delivery to a private property
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Figure 20: City selection process for airport shuttle use case
Main steps
Proceeding
Selection of most suitable cities based on identified metrics
Ranking of cities based on KPIs
Infrastructure feasibilityassessment
Timing feasibility assessment
III
IIIIV
1 Availability of inner-city space for heliports
1 Number of announced pilot projects2 Expected trip
volumes
2
3 Availability of mobility offer for onward journey (e.g., e-scooter, car-sharing)
2 Timeline of known pilot projects
3 Implementation feasibilityuntil 2025
1 European cities with high population (>300 k)
2 Availability of an airport)
3 Number of travellers between the airport and the city centre (>25k passengers/ day)
4 High GDP/capita level (>35k PPP in EUR)
1 City size
3 Distance between airport and city centre
Used for informative purposes only and not for
selection
4
6
5
7
Travel time between airport and city centre with fastest alternative travel type in the rush hour
Congestion rate
Taxi price
Suitable weather conditions (% of weather causes in total arrival days, precipitation in mm per year)
Availability of river, highway or corridor for noise avoidance
The detailed evaluation per use case is provided in the Appendix 2. Please find further information on the viability 1) for airport shuttle use case, 2) for sightseeing use case, 3) for first aid use case, 4) for Last-mile delivery, 5) for Medical supply delivery, 6) for fixed metropolitan/regional network.
After applying this process, a number of cities remained which were further prioritised according to a KPI system tailored to the respective use case. Figure 21, on the example of the airport use case, shows how pre-selected cities were ranked by KPIs to arrive at the 15 highest priority cities. The KPIs included: city size; expected number of trips; distance between the airport and city centre; travel time between the airport and the city centre with the fastest alternative travel type (e.g. taxi, car, or public transport) in rush hour; congestion rate; taxi cost for the journey to the airport; and suitable weather conditions (percentage of weather causes in total arrival delays, precipitation in mm per year). A weighting factor was assigned to each KPI to adjust that KPI’s impact on the overall ranking score.
2.2 Target market identification 43
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25%
WeightingKPI
Time saving
10%7 Suitable weather conditions (% of weather causes in total arrival delays, precipitation in mm per year)
5 Congestion rate
3 Distance between airport and city centre
4 Travel time between airport and city centre with fastest alternative travel type in the rush hour
25%City size1
25%Expected trip volumes2
6 Taxi expenses for ride from airport to city-centre
15%
Milan 72.1
Berlin 78.3
Madrid 74.8
Rome 75.4
Vienna 70.4
Barcelona 71.4
Prague 72.8
Munich 75.3
Budapest 73.8
Dublin 70.8
Hamburg 62.8
Brussels 67.8
Amsterdam 65.6
Bucharest 67.4
Frankfurt am Main
59.9
Lyon 60.8
Stockholm 63.0
Warsaw 66.8
Stuttgart 63.9
Dusseldorf 56.8
Bonn 59.6
Ranking of cities based on further KPIs
Toulouse 55.2
Copenhagen 56.2
Cologne 57.8
Bologna 59.8
Helsinki 58.2
88.2
City
Ranking (100 = best suitability for UAM)
Paris
Figure 21: Target cities ranking process for the airport shuttle use case
Based on this methodology, 90 potential target markets (15 cities x 6 use cases) were identified for initial OEM introduction (‘long list’, see Figure 22 below).
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Figure 22: Potential target markets for the six prioritised use cases
City shortlist
D
People transportation Cargo use cases
C HA 3 Q
ParisBerlin RomeMunichMadridBudapestPragueMilanBarcelonaDublinViennaBrusselsBucharestWarsawAmsterdam
ParisRomeAmsterdamVenicePragueBarcelonaFlorenceBudapestBerlinFrankfurt am MainStockholmMadridAthensNiceLisbon
Sightseeing
Belgian central metro (Brussels) Rhein-Ruhr region(Cologne, Düsseldorf, Duisburg, etc.) Rome metropolitan regionMilan metropolitan regionBarcelona metropolitan areaRhein-Neckar region(Mannheim, Karlsruhe, Heidelberg, Pforzheim, etc)Stuttgart metropolitan regionOresund region(Copenhagen, Hillerod, Malmo, Lind)Munich metropolitan regionVienna metropolitan regionParis metropolitan regionRhein-Main region(Frankfurt, Darmstadt, Mainz, etc.)Warsaw metropolitan regionStockholm metropolitan regionNoord-Brabant region(Eindhoven, Tilburg, Breda, etc.)
Fixed metropolitan network (<120km)
ParisBerlinHamburgLyonMarseilleNiceGenovaPragueBrnoBudapestNurnbergStuttgartMilanRomeDublin
First aid
RotterdamHamburgHelsinkiTallinnBonnHanoverCologneDortmundDresdenDusseldorfEssenLeipzigBratislavaPragueMurcia
CracowWroclawBucharestWarsawPoznanSofiaBudapestDublinPrahaBrnoVilniusHamburgBerlinGdanskParis
Last mile delivery
Airport shuttle
Medical supplies
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Figure 23: Guiding principles for city selection
Aspiration RationaleGuiding principle
Different regions
Regional differences will influence the societal acceptance of UAM
1. Our research focuses on different regions of the European Union
Different Culture
Different cultural values will have an impact on the societal acceptance
2. We investigated regions with possibly large cultural differences
Public awareness about upcoming pilot projects will affect the societal acceptance in small/medium cities
4. Medium and small size cities were included, if they already announced the attention to start pilot projects
Differentsizes
High traffic density, noise levels and UAM risks will significantly influence the societal acceptance in large cities
3. The focus of the research was on large cities with the population >100 K inhabitants
The societal acceptance will vary for different city archetypes
5. We analysed different cities archetypes (dense/wide-spread, high/medium-income, urban/suburban city)
Different Archetypes
City selection for the survey
This long list was reduced to six major cities, taking into account the use cases with top rankings and city-average ranking scores (across all use cases where the city was on the top 15 list). In addition, five guiding principles were established to help ensure that the selected major cities were representative of different regions, cultures and city archetypes (see Figure 23).
-
46
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Figure 24: Final city selection and ranking
Our final cities selection covers all demographics, cultures and cities archetypes dimensions
Top cities from KPI based evaluation X
City added to fulfil the guiding principlesX
1. Most relevant use case
Ranking Use case City archetypeCity/Country Country/Region
1 Paris
2 Rome
4 Hamburg
3 Barcelona
6 Oresund region(Copenhagen, Hillerod, Helsingor, Malmo, Lund)
5 Budapest
100 Large, very dense, high–income city
Airport shuttle1
Sight-seeing, First aid Central Europe
87 Large, medium dense, medium-income city
Sight-seeing1
Airport shuttle, First aidSouth Europe
75 Large size, medium dense, medium-income city
First aid1
Airport shuttle, Sight-seeing Central Europe
79 Large, very dense, medium-income city
Sight-seeing1
Airport shuttle, First aid, Medical supply
South Europe
20 Network of wide spread medium size cities
Fixed metropolitan network 1
North Europe
68 Large size, medium dense, medium-income city
Medical supply1
Airport shuttle, First aid, Sight-seeing
East Europe
Target cities selection › Final cities
As a result of this process, Barcelona (Spain), Budapest (Hungary), Hamburg (Germany), Milan (Italy), Paris (France) and Øresund (cross-border region of Denmark and Sweden) were chosen as sites for the quantitative survey (see Figure 24). For further details on use cases and the metrics used for the survey city-selection process, see Appendix 2.
-Cities selected for the survey• Barcelona• Budapest• Hamburg• Milan• Paris• Øresund cross-border region between Denmark and Sweden
475. Survey-based assessment of societal benefits and concerns for European citizens
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The stated goal of the survey was to assess and understand the most important societal-acceptance drivers for UAM across cultures and regions in the European Union, including perceived benefits and concerns. This served both to complement available data from literature and to confirm this data for the specific EU environment. The results of this survey will further support the impact assessment and regulatory work of EASA.
The survey itself contained three parts:
• A quantitative survey, with the participation of 3,690 citizens across six European cities, through a web-based questionnaire;
• A qualitative survey, consisting of one-hour interviews with more than 40 stakeholders at local, national and European level, informed by the results of the quantitative survey and aimed at better understanding the perspectives of different stakeholders;
• A special noise perception survey with 20 participants was initiated to gain even more insights on how the noise of UAM aircraft may be perceived by the public.
The following chapter describes the overall survey methodology (3.1) and provides the ten key survey results (3.2), aggregating results from across three parts.
3. Survey-based assessment of public acceptance of UAM in the EU
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Chapter 3.1 is divided into three parts: First, the methodology for the quantitative survey is explained (3.1.1) in detail, including information on how participants were chosen, how the survey was structured and how the questions were defined. This section also provides a deep dive into the methodology of the conjoint (or: trade-off) analyses. Then follows an overview of the qualitative survey methodology (3.1.2) and the methodology for the noise acceptance study (3.1.3).
3.1.1 Quantitative survey methodology
Information on the panel of participants
The participant target for the quantitative survey was at least 600 individuals from each of the six cities being surveyed. To ensure that participants were sufficiently representative of the populations of the surveyed cities, nationally representative distributions were chosen regarding gender, age, and employment status. As a sample can never be perfectly representative of a population’s distribution, technical criteria were used to ensure the margin of error was kept as small as possible. Screening questions were used at the beginning of the questionnaire to ensure the fulfilment of quotas and to identify other demographic features. These questions related to, for
instance, age, gender, type of household, place of residence, etc; More information on these screening questions can be found in the Appendix (Questionnaire). The final demographic distribution of participants be seen in Figure 25.
Advantages of an online panel Online samples are considered very
representative:
• A broad section of the population
reacts to online surveys
• Office workers are easier to reach
(filling survey during the workday
and can participate at a time that’s
convenient for them)
• Respondents provide more authentic
and detailed answers to open-ended
questions (as they may take the time
to reflect on their responses)
• Various visualisation options
(videos, logos, product images,
shelves, advertisements, TV spots,
radio spots, etc.) arise and survey
questions are better understood by
participants.
3.1 Survey methodology
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Figure 25: Panel selection across cities
Panel composition shows that representative distribution and quotas are met in total panel
16
17
17
18
18
14
35-44
18-24
55-64
25-34
45-54
65-75
Education, % Gross household income per year
21
45
23
11
High (> 60k EUR)
Low (< 20k EUR)Medium (20k - 60k EUR)
Prefer not to say
Female
Male
51
49
Employment status,%
Full time (30+ h) incl. self-employed
53
Part time or student 19
Not working, retired and other
29
Low (up to higher schooling)
37
Medium (up to finished college or university)
44
High (post-graduates or higher)
19
Singles 21
Couples 46
Families 33
Panel size = 3690 participants
Gender, %Age, % Family type, %
To summarise the figures shown in Figure 25: : • Total numbers of participants across the six cities was 3,690; • Balanced gender distribution, as number of male and female participants were nearly the same (0.2 percent diverse,
other, or preferred not to answer). The shares of male and female participants had to be at least 48 percent each. As women usually tend to be more responsive to online panels than men, it was important to maintain a balance between the genders, but a margin of error of 1 to 2 percent is considered a statistically acceptable range;
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• Participant age was relatively evenly distributed between 16 and 75 years. At least 15 percent of participants were required to be in each of the age groups 18 to 24, 25 to 34, 35 to 44, 45 to 54 and 55 to 64, and 10 percent in the 65 to 75 age group. The age group 65 to 75 years was not required to be as large as other groups as this group is generally less responsive to online panels and will be less affected by innovations in UAM, which are currently still in their infancy and will take years to develop;
• Most of the participants (46 percent) were employed full time (30 hours or more per week), 9 percent employed part time (up to 30 hours per week), 7 percent were self-employed (business owners, freelancers), 9 percent were college or university students or apprentices, 4 percent were homemakers, 16 percent were retired, 8 percent were jobseekers or other, and 1 percent preferred not to say;
• 99 percent of participants had EU citizenship; participants had to reside in the city or region where the survey was conducted, as the aim was to reflect the perceived benefits and concerns of residents potentially affected by the rise of UAM;
• The type of household was diverse as well: singles (24 percent), participants with two persons in household (51 percent), with three (16 percent), four (7 percent), five or more persons in the household (2 percent) participated;
• On the level of education, 2 percent had no school-leaving certificate, 9 percent finished basic schooling, 27 percent finished higher schooling (10 or more years), 13 percent had college or university education (no degree), 29 percent have a college or university degree (e.g. diploma or bachelor’s degree), 19 percent have a postgraduate degree or higher (e.g. master’s degree, PhD), 1 percent preferred not to say;
• The total gross household income per year shows that 21 percent of the participants receive less than EUR 20,000, 26 percent EUR 20,000 to 39,999, 19 percent receive EUR 40,000 to 59,999, 9 percent receive EUR 60,000 to 79,999, 5 percent receive EUR 80,000 to 99,999, 4 percent receive EUR 100,000 to 119,999, 2 percent receive EUR 120,000 to 139,999, 1 percent receive EUR 140,000 to 160,000, 2 percent receive over EUR 160,000, while 11 percent preferred not to say;
• The replies on employment industry show that participants work in: grocery or other food retail or manufacturing (3 percent), automotive and transport (4 percent), public sector and administration (6 percent), banking and finance (7 percent), clothing manufacturing or retail (2 percent), education (7 percent), healthcare (7 percent), computer science or IT (9 percent), in another field (25 percent) or are unemployed (30 percent). The maximum allowed share of the non-working population was 35 percent, to avoid a skewed distribution towards this very responsive group;
• To make sure we compiled a solid database on potential users of UAM, special attention was paid on ensuring to get a minimum number of respondents with generally positive attitudes towards UAM and who were identified classified as potential users. A minimum of 120 participants per city were identified as potential users of drone delivery – same for air taxis. A minimum of 200 participants per city were identified as potential users of either drone delivery or air taxis. And a minimum of 240 participants per city were not identified as potential users of UAM;
• People working in advertising, media, PR and marketing may typically encounter surveys and statistical models in their day-to-day work. They understand the underlying methodology and levers and this could have an influence on their responses and thus the outcome of the survey. Therefore, these professional groups were categorically excluded.
The survey was translated into the local languages of the participants (Spanish, Hungarian, German, Italian and French) to ensure understanding across the different cities and regions. The exception was for participants in the Øresund region where the survey was conducted in English, as non-native English skills are very good in Scandinavian countries according to the Education First English Proficiency Index4.
4 https://www.ef.com/wwen/epi/
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Information on the questionnaire structure and question types
The questionnaire was designed to assess, understand, and measure the most important societal-acceptance drivers for UAM, including perceived benefits and concerns and what it would take to increase societal acceptance. The questionnaire included 36 questions; the response time was estimated at twenty-five minutes.
Two use cases that are considered easy to imagine and self-explanatory for non-experts were used in the quantitative survey to determine levels of acceptance: the delivery of goods in the low single digit kilogram range by drone and the transport of passengers by air taxi. An in-depth analysis was conducted to measure the relative acceptance levels across cultures of three key concerns identified through the literature review: the perceptions of safety, noise, and visual annoyance in an urban environment. Finally, the questionnaire addressed the general attitude and expectations of respondents towards regulatory authorities. For an English version of the questionnaire that was distributed online to the participants, please refer to the Appendix.
The first part of the survey ensured that participants met the predefined criteria (see the predefined quotas above). An informational video of 1 minute and 36 seconds was presented to give participants’ prior information as well as general and common understanding of UAM. The use cases shown in the video were passenger transport by air taxi, express delivery of food by drone, transport of emergency medical services to the scene of an accident, and delivery of medical supplies to a hospital. The selection aimed for a balanced representation of commercial and public service use cases, drone and passenger use cases, as well as use cases both with a pilot on board and remotely piloted. The vehicles depicted in the video were invented and did not correspond to any industrial product existing or in development. The objective was to give a general feeling and idea, rather than to reflect actual technical accuracy. The video concluded with the message that Urban Air Mobility is coming soon to Europe. The video did not include any sound other than music, as noise perception was evaluated in a separate survey.
Subsequently, this section checked whether participants could be considered a potential user for either or both of delivery of goods by drone and/or transport of passengers by air taxi.
The subsequent parts of the survey focused on collecting insights about the following topics (in order):
• General attitude towards UAM• Delivery by drone• Passenger transport (air taxi)• Regulators and their role• Further understanding of security and environmental aspects• Additional demographic questions
For more information on the structure of the questionnaire, see Appendix 2.
The online questionnaire was divided into 6 areas:1. Making participants familiar with what
UAM means and assessing participants’
general attitude towards new techno-
logy and UAM use cases
2. Testing acceptance of delivery drones,
3. Testing acceptance of passenger trans-
port (air taxis)
4. Understanding their attitude towards
regulators and their expectations
5. Understanding security and environ-
mental aspects and concerns
6. Asking for additional demographic data
533. Survey-based assessment of public acceptance of UAM in the EU
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Deep dive into choice-based conjoint (trade-off) analysis
Conjoint analysis is a statistical technique that models the behaviour of survey participants in choice / trade-off situations. Among other things, it helps to explain and forecast the level of readiness for new technologies where trade-offs between objectives need to be made. In a survey situation, participants are asked to indicate their preferences when faced with different alternatives. The aim is to find out which factors are relevant to a decision and to what extent they influence that decision.
A conjoint setting is characterised by its attributes and the levels of those attributes. Attributes are characteristic properties of, e.g. products, services, or scenarios. In product design, typical attributes may be price, brand, and durability. Attributes should be relevant to decision making, consist of at least two levels with varying values, and are expected to influence preferences between products, services or scenarios. In product design, typical attributes may be price, brand and durability. Levels are expressions of the attributes, i.e. unambiguous, mutually exclusive and realistic possibilities of how an attribute could materialise. Levels for, e.g. the attribute price would simply be the different price points.
Participants are offered a choice between different bundles, in which each attribute is assigned one level only. To continue the above example, a bundle would be a theoretical product described by its price, brand and expected durability. As the number of attributes and levels to be assessed significantly influences the sample size and number of choices to be made, the number of distinct attributes and levels should be limited to keep the scope of the choice-based conjoint analysis manageable.
In a survey setting, the process for conducting a choice-based conjoint analysis is as follows: participants are shown a small number of different bundles represented by choice cards (see Figure 26), and are asked to choose their preferred bundle. This step is repeated several times.
Figure 26: Example of a choice in choice-based conjoint analysis
Safety One drone has the same likelihood of hitting a pedestrian as one car
One drone has 1/10ththe likelihood of hitting a
pedestrian as one car
One drone has 1/100ththe likelihood of hitting a
pedestrian as one car
Noise One drone Is as loud as a leaf blower (~90-r dB,
unbearable)
One drone is as loud as a leaf blower (-90+ dB.
unbearable)
One drone is as loud as a car driving by at city
speed (~65 dB, noticeable)
Visuals 1 or 2 drones per hour in one's field of view when walking down a street
~5 drones per hour in one's field of view when walking down a street
20+ drones per hour in one's field of view when walking down a street
Select Select Select
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Due to the high number of possible combinations of attribute levels into bundles, participants will not see every possible bundle, and will not be asked to compare every bundle to every other bundle. However, preferences can be extrapolated based on a few choices.
A specific two-stage process is used to estimate valid/stable utilities for each respondent: a latent class (LC) segmentation is followed by a hierarchical Bayes (HB) utility estimation within each latent class analysis (LCA) segment.
The model assigns a utility to each level (the expressions of the attribute). The utility describes numerically how (negative for rejection, positive for approval) and to what extent a level impacts decision making (small absolute value for little influence, large absolute value for great influence).
The greatest increase in utility within an attribute is equivalent to the greatest gain in approval rating (i.e. from the left-hand side to right-hand side of a number scale).
Arranging the utilities on a scale from -3 to 3 gives an overview of which levels lead to rejection and which to approval, e.g. level A.4 in Figure 27 has the greatest approval rating for attribute A. The ideal bundle would consist of levels A.4, B.4, C.4 and D.4. However, in a real-world setting this particular bundle might be unrealistic, and trade-offs may need to be made. This raises the question about which levels are still considered acceptable, i.e. what is the lower boundary. For example, a bundle composed from the levels to the right of the respective greatest increases, i.e. A.4, B.3, C.2 and D.1 in Figure 27, might provide an acceptable approval rate. However, this logic should be applied with caution to real-life applications and bundles should always be chosen with care.
Level B.1 Level B.2 Level B.3 Level B.4
Attribute B
Level C.1 Level C.2 Level C.3 Level C.4
Attribute C
Level D.1 Level D.2 Level D.3 Level D.4
Attribute D
-3.0 0.0
Level A.1 Level A.2 Level A.3 Level A.4
Attribute A
3.0
Utilities of attributes
Figure 27: Schematic illustration of utilities in choice-based conjoint analysis
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Conjoint analysis was chosen for the joint assessment of concerns regarding safety, noise, and visual annoyance in two settings: the operation of drones and the operation of air taxis. The aim was to avoid participants choosing the option that indicated the least change from the status quo when asked about desirable levels. In the conjoint analysis they are forced to make trade-offs between three scenarios, thereby indicating real preferences and acceptable levels. The questions used can be found under B7 and C7 in the Appendix.
The bundle of levels accepted by the majority will serve as a basis for future regulatory projects. The initial setting of levels is therefore of particular importance. On the one hand, levels need to be specific enough to form a solid basis for specifications in regulatory projects; their formulations, on the other hand, need to be graspable for non-experts and relatable in a survey situation.
Safety
Two different scales for the safety attribute were selected because the air taxi use case poses risks to both passengers and pedestrians, whereas the drone use case poses risk solely to pedestrians.
The following levels were selected for the operation of drones: five-times higher likelihood of one drone hitting a pedestrian as one car; the same likelihood; one-tenth of the likelihood; and one-hundredth of the likelihood. The safety standard for the first level would translate to about 200 fatalities per year in Europe by 2025, compared to 22,800 fatalities caused by cars in the 27 EU member states in 20195 (i.e. 200 fatalities from drones would lie in a range of 1 percent of the fatalities from car accidents). The best level for the drone safety standard (a hundredfold improvement compared to passenger cars) lies within a factor of two of today’s commercial aircraft safety standard (calculated on a passenger-kilometre basis and assigning a theoretical passenger to a drone).
The following levels were selected for the operation of air taxis: safety standards comparable to motorcycles (approxi- mately 5 fatalities per billion passenger kilometres), cars (approximately 2)6, buses (approximately 0.05)7, and com-mercial aircraft (approximately 0.01)8. Motorcycles are widely regarded as an unsafe mode of transport, and commercial aircraft as one of the safest.
Noise
For the noise attribute, the following levels were selected to cover both the operation of drones and the operation of air taxis: volume of a leaf blower (over 90 dB, unbearable), volume of a truck driving by at city speed (roughly 82 dB, disturbing), volume of a car driving by at city speed (approximately 65 dB, noticeable), and volume of a bicycle riding by at city speed (around 57 dB, barely noticeable). By comparing scenarios to an example from everyday life, participants can imagine the background noise; moreover, the decibel indication can be used to inform a noise specification. For the first and loudest level, a noise was selected that is quite common in urban environments but not permanently conceivable as background noise. For the last level, a noise was selected that is not disturbing but still realistic for drones and air taxis.
5 https://ec.europa.eu/commission/presscorner/detail/de/qanda_20_10046 Eurostat7 Eurostat8 Eurostat
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Figure 28: Stakeholder interviewee overview
28
Stakeholders interviewed
Local level
Mayor and municipalities servicesLocal environmental protection associationsLocal traffic and transport authorityLocal resident association/Real-estate ownersEmergency response organizationLocal airport, local ATCLocal urban and city plannersLocal chamber of commerceLocal police
National and European level
Airports Council International (ACI)National governmental authorities for aviation (e.g., DCGA)European environmental protection associations Alliance for new mobility Europe (ANME)An insurance providerEuropean CommissionThe European helicopters associationEuropean business aviation associationCANSOSmart City InitiativeThe European Cockpit Association
Visuals
For the visuals attribute, the following levels were selected for both the operation of drones and the operation of air taxis: more than 20 flying vehicles per hour in one’s field of vision when walking down a street; around 10 vehicles; around 5 vehicles; and 1 or 2 vehicles. On a typical day and in a typical residential area, roughly 1 to 2 aircraft per hour are visible in the sky today. Helicopters, too, can be seen flying above cities in Europe and hospital pads are estimated to be busy at 1 to 2 landings per hour. The last level is chosen as a realistic lower limit. Multiplying this number by 20 for the first level amounts to a massive change from today but is in the range of the projected number of drones in urban areas in 2025.
Questions B8 and C8 in the Appendix ultimately serve to find upper and lower limits for the overall acceptance rate. For this purpose, the acceptance rate (without comparison or choice) for the bundle of consistently best levels is queried, as well as the acceptance rate for the bundle of consistently worst levels. The results from the conjoint analysis can then be fit into these boundaries.
3.1.2 Qualitative survey methodology
After there was a clear picture on the results from the quantitative survey, further qualitative interviews with more than 40 local, national, and European stakeholders took place (see Figure 28). To prioritise and select the stakeholders for these interviews, the long list of identified stakeholders from the literature review was taken and assessed along the three dimensions of concerns, level of influence and level of support for UAM. The stakeholders with the highest concerns and level of influence, as well as the lowest level of support for UAM, were ranked highest. The objective was to get their differentiated and specialised point of view on benefits and concerns around UAM. Around half of the stakeholders interviewed were local, covering all cities in which the survey took place and all stakeholder groups. In most cases, interviews with representatives of at least two of the cities were done in order to capture potential local differences.
28
Stakeholders interviewed
Local level
Mayor and municipalities servicesLocal environmental protection associationsLocal traffic and transport authorityLocal resident association/Real-estate ownersEmergency response organizationLocal airport, local ATCLocal urban and city plannersLocal chamber of commerceLocal police
National and European level
Airports Council International (ACI)National governmental authorities for aviation (e.g., DCGA)European environmental protection associations Alliance for new mobility Europe (ANME)An insurance providerEuropean CommissionThe European helicopters associationEuropean business aviation associationCANSOSmart City InitiativeThe European Cockpit Association
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3.1.3 Noise test methodology
Both the qualitative and quantitative surveys identified noise as one of the major concerns for societal acceptance of urban/advanced air mobility. UAM aircraft have a variety of noise profiles that differ from those of traditional aircraft. Characteristics of their noise signature such as tonality could potentially increase annoyance. Other health impacts associated with introducing this entirely new sound source into urban and suburban environments are not widely known. UAM aircraft pose another layer of complexity since, compared with other types of aircraft, they will fly closer to where people live and work.
Understanding how noise from UAM aircraft is perceived will therefore be essential to the introduction of this new technology. Designs of aircraft, legislation and planning for UAM infrastructure and routes will all be dependent upon understanding reaction to the sound.
To begin to understand people’s response to UAM sound and investigate how a more comprehensive study would operate, a pilot study was undertaken with Arup featuring listening tests leveraging the Arup Soundlab. This is described in the following section.
Use of sound demonstrations for understanding environmental sound
Originally, Arup Soundlab was conceived and developed to inform the design of some of the world’s best arts and culture venues. More recently, SoundLab has been used to enable clients and stakeholders to experience the impacts of major infrastructure projects during the design and planning process, helping to shape better outcomes. Through the process of continuous development, SoundLab pairs advanced visualisation with novel aircraft noise synthetisation tools to enable noise assessment in the early stages of aircraft design. SoundLab has also developed methods and transportable facilities for engaging people in their local areas, delivering sound demonstrations for schemes including aviation, railways, highways and windfarms. Compared with traditional methods of engagement on environmental sound issues, sound demonstrations have many advantages:
• helping identify stakeholders’ needs and concerns; • facilitating dialogue on implications of noise on public health and wellbeing; • increasing transparency on how stakeholders could be affected by a proposed development or new noise source; • building trust through impartial advice and integrity;• ensuring proposals are inclusive and accessible; and• supporting data-driven decision making.
The pilot study
The pilot study aimed to: • Investigate whether sound demonstrations would be an appropriate way to engage with and understand people’s
responses to UAM sound;• Test and develop approaches to undertake the tests, with a view to carrying out a larger study; and• Gain initial insight into people’s perceptions of UAM sound, noting that the sample would be far too small to yield
statistically reliable results.• Due to travel restrictions in place at the time as a consequence of the COVID-19 pandemic, the study was carried out
using a transportable facility (MLab) currently installed in Arup’s office in Amsterdam. As in SoundLab, MLab uses 3D ambisonic sound, focused at the listener’s head height, from an array of 12 loudspeakers, four each at floor level, head height and overhead (plus bass loudspeakers) – see Figure 29 below.
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Figure 29: Noise auralization setup in Arup MLAB in Amsterdam
A total of 20 listeners took part over a period of two days in April 2021. Each test was carried out with one listener at a time. Listeners were guided through the tests in the following way:• The purpose of the tests and how they were being undertaken was explained. The listeners were told that the
research was to gauge responses to different transportation sounds; drones and air taxis were not mentioned; • The listener was settled into the listening environment by hearing background sounds of an outdoors urban location;
A recording made in Dam Square, Amsterdam was used, to reflect the soundscape in the city in which the tests were conducted. This background sound contained no transportation sound and was reproduced at a level of 55dBLAeq. It was played continuously as a backdrop to the transportation sounds;
• An image of Dam Square was presented on a display screen to help localise the listener. No visual representation of the sound sources was included in the test to avoid objectivity bias;
• Sounds from the following sources were each reproduced at a level of 80dBLAmax,F: jet aircraft, helicopter, bus, motorbike, large multicopter drone, smaller multicopter drone, air taxi and synthesised air taxi with different acoustic characteristics to the others. Additionally, the air taxi sound was reproduced at two lower sound levels (60 and 70 dBLAmax,F). Each sound source recording was played for 30s. Normalisation of the sound levels to the maximum level may have resulted in the sound levels for each source being different, if they had been quantified using a different metric, such as the LAeq, due to the different durations and rise and decay times of the sounds;
• The listeners were asked to rank each sound according to how much the sound would bother, annoy or disturb them on a scale of 0 (not at all) to 10 (extremely) as Figure 30 shows. They were asked to consider their responses assuming the sound occurred five times in one hour. In addition to the annoyance scale, they were also asked to provide comments on the aspects of the sound that had contributed to their rating. Thirty seconds were allowed for each response before playing the next sound.
Figure 30: Scale to determine if sound was found to be bothering, annoying or disturbing
On scale of 0 (not at all) to 10 (extremely), how much would the sound bother, annoy or disturb you?
Noise 1
0 1 2 3 4 5 6 7 8 9 10Not at all Extremely
page
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3.2 Survey results (10 key findings)
The overall objective of the survey was to generate new insights on societal acceptance of UAM by European citizens and so support the future impact assessment and further regulatory work by EASA.
The following sections summarise the 10 key findings from the survey conducted between January and April 2021. For each key finding, the combined insights from the quantitative survey, the qualitative interviews and the noise test are presented. For the detailed survey results, please go to the EASA website.
3.2.1 A positive initial attitude to UAM throughout the EU
Homogeneous results
The survey was designed to capture potential divergence of opinion within various sub-groups, notably through screening questions covering age, household composition, affinity to new technologies, geographical/cultural differences, etc. (see Information on the panel of participants above). Unexpectedly, the results demonstrated homogeneous replies: a level playing field throughout Europe, with no major deviation between the respondents of the six cities (see Figure 31), and no major deviations according to age, household composition or affinity to new technologies or other differentiating criteria (see Figure 32). This was confirmed by the qualitative interviews.
3.2 Survey results (10 key findings)
Source: EASA UAM societal acceptance survey questions S6. How likely are you to make use of delivery of goods by drone (i.e.,delivery of parcels from an online shopping platform to a nearby delivery hub, your garden or private property or a publicly accessible area), if it were offered in your city? Please assume that delivery by drone would cost about double today’s standard shipping fees and ensured guaranteed delivery within 2 hours from the time you place your order. S7. How likely would you be to use an air taxi (i.e., a flying vehicle that transports passengers from A to B) for a 25-50% higher price than current road passenger transport options like conventional (road) taxis or Uber-like offerings, if you assume the trip could be made in half the time in the air taxi?
25%
25%
26%
29%
21%
28%
22%
33%
33%
35%
36%
32%
35%
27%
11%
7%
8%
14%
7%
12%
17%
18%
16%
11%
17%
14%
24%
23%
42%
46%
43%
38%
41%
41%
44%
35%
35%
40%
33%
36%
29%
36%
22%
22%
23%
19%
31%
18%
17%
15%
16%
14%
14%
18%
12%
14%
Total
Öresund
Barcelona
Budapest
Hamburg
Milan
Total
Paris
Barcelona
Öresund
Budapest
Hamburg
Milan
Paris
Rather likelyRather unlikelyNot at all likely Very likely
64%36%
49%51%
68%32%
51%49%
67%33%
54%46%
57%43%
47%53%
72%28%
54%46%
59%41%
41%59%
61%39%
50%50%
… delivery drones
… air taxis
Likelihood to try out…
X% Sum +X% Positive difference to avg -X% Negative difference to avg
Figure 31: Cities surveyed indicated a similar likelihood of using the services
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Only small differences were noticed:• In general, respondents from the South of Europe (Milan, Barcelona) demonstrated a more positive attitude across
all question types than those from the Northern part of Europe (Hamburg, Oresund region);• On the readiness to use drone services: respondents from Milan (+8 percent) and Barcelona (+4 percent) had more
positive attitude compared with the average, while Hamburg (-7 percent) and Oresund region (-5 percent) were more critical;
• On the readiness to use air taxi services: respondents from Milan (+5 percent) and Barcelona (+2 percent) were amongst those declaring a more positive attitude, and citizens from Budapest were more likely to try an air taxi service (+5 percent compared to average). Citizens from Oresund (-8 percent) and Hamburg (-2 percent) showed lowest interest in air taxi services.
The deviations in demographic groups and defined subgroups followed expectations: • The positive demographic groups included younger people, such as the age group 25-34 (+7 percent more likely to
try out drones, and +10 percent more likely to try air taxis compared to the average), men (+5 percent, +7 percent),
Figure 32: Overall interest in UAM services by subgroups deviating significantly from average
…
…
Source: EASA UAM societal acceptance survey questions S6. How likely are you to make use of delivery of goods by drone (i.e., delivery of parcels from an online shopping platform to a nearby delivery hub, your garden or private property or a publicly accessible area), if it were offered in your city? Please assume that delivery by drone would cost about double today’s standard shipping fees and ensured guaranteed delivery within 2 hours from the time you place your order. S7. How likely would you be to use an air taxi (i.e., a flying vehicle that transports passengers from A to B) for a 25-50% higher price than current road passenger transport options like conventional (road) taxis or Uber-like offerings, if you assume the trip could be made in half the time in the air taxi?
25%
19%
15%
18%
31%
30%
35%
33%
29%
19%
26%
37%
37%
11%
7%
5%
6%
16%
16%
23%
18%
12%
5%
9%
24%
34%
42%
46%
46%
47%
37%
40%
33%
35%
41%
43%
40%
30%
24%
22%
28%
34%
30%
16%
14%
9%
15%
18%
33%
25%
9%
5%
Families
Total
Digital adopters
Target group for express delivery by drone
Total
Age group 65-75
Digital laggards
Age group 65-75
Singles
Age group 25-34
Target group for airport shuttle
Digital adopters
Digital laggards
Not at all likely Rather unlikely Rather likely Very likely
… delivery drones
Likelihood to try out…
… air taxis
-11%
-10%
-22%
36% 64%
+10%
+16%
+13%
-10%
-20%
51% 49%
+10%
+27%
+15%
X% Sum +X% Positive difference to avg -X% Negative difference to avg
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the high income group (+6 percent, +7 percent) as well as digital adopters as participants accustomed to using other innovative services (+16 percent, +15 percent). It is possible that families (+10 percent each) were more positive than singles because the perceived advantages of UAM services (e.g. time saving) are felt to be more critical for a couple with a double burden of work and educational activities for their children;
• As expected, the subgroup defined to be the target group for services of delivery drones and air taxis were amongst those with the most positive attitude when asked how likely they were to use available services (+13 percent, + 27 percent).
On the other hand, the slightly more reluctant groups included singles (-10 percent, -8 percent), older people such as the age group 65-75 (-11 percent, -10 percent), women (-5 percent and -6 percent) and digital laggards (-9 percent and -5 percent).
Positive initial perception
Early in the survey, participants were asked to indicate what their overall perception would be if Urban Air Mobility solutions were introduced in their cities.
Overall, the perception of UAM was positive: most (83 percent) of the respondents felt (very or rather) positive about the introduction of UAM overall. Across the surveyed cities, only 3 percent of the respondents had a negative perception of Urban Air Mobility and will probably be hard to win round to the introduction of UAM (Figure 32a).
143 54 29
Overall, 83% of respondents have a very positive or rather positive attitude towards UAM…
General attitude towards urban air mobility, %
Source: EASA UAM societal acceptance survey question S5. What would be your overall perception if urban air mobility solutions (such as those shown in the video) were to be introduced in your city? Please select one answer.
83%
xx% Sum
17%
Very negative
Rather positive
Rather negative
Very positive
Figure 32a: Vast majority of respondents were positive on UAM
The qualitative interviews also indicated that the general attitude towards UAM is mostly positive. Surprisingly, this was even true for participants who could have been expected to have most concerns – for instance, those involved in security or environmental matters. The interviews indicated that UAM is seen as an exciting innovative development and the fact that Europe may be playing a leading role in this domain is triggering positive consideration and goodwill. However, the survey revealed that most stakeholders have not yet been exposed to UAM matters so far, except for those cities with pilot projects (e.g. Hamburg or Paris), and generally lack information on the topic.
Readiness to use UAM
As shown in Figure 32b, the results showed that a large share of the population would be interested to use UAM
services. 64 percent would be interested in using drone delivery and 49 percent would be interested in using an air taxi. 43 percent would be interested in using both, 71 percent are likely to make use of at least one service. Only 29 percent would not use either of these services if they were available in their respective cities.
633.2 Survey results (10 key findings)
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Figure 32b: Vast majority is positive on UAM
Level of comfort
The questionnaire also verified participants’ basic comfort level with the idea of delivery drones and of manned and unmanned air taxis. To this effect, participants were asked to rank their level of comfort with respect to different scenarios (see question C4, Appendix).
Air taxis pose a potential safety threat not only to the passengers using them. Pedestrians will be affected by drones and air taxis, despite not necessarily choosing to use them themselves, and so the hypothesis was that the perceived safety of pedestrians will have a significant impact on the societal acceptance of drones and air taxi operations.
Overall, the results from the questionnaire were positive. The majority of respondents indicated that, as pedestrians, they would feel safe with drones and manned air taxis flying above their heads.
However, the results also showed that pedestrians always felt safer considering manned aircraft than considering unmanned ones, no matter the size or the characteristics of the aircraft. Therefore, the share of people feeling safe as pedestrians with manned air taxis (70 percent) flying above their heads was much greater than with unmanned ones (44 percent) – and still higher than those who feel comfortable with unmanned delivery drones (56 percent), as Figure 33 shows. As expected, the number that would be likely to try a manned air taxi (75 percent) was higher than those who would try an unmanned one (43 percent). It is also no surprise that those participants identified to be potential air taxi users had a higher level of comfort with manned air taxis (+13 percent) than the average.
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D
UAM rejecters: 29% not likely to become users of either use case (defines subgroup)
A
Broad users: 43% likely to become users of both services
Likelihood to use air taxis
Like
lihoo
d to
use
del
iver
y dr
ones
A
Potential UAM users: 71% likely to make use of at least one service (defines subgroup)
B C+ +
B
C D
A
Potential air taxi users: 49% likely to make use of at least one service (defines subgroup)
C+
A
Potential drone delivery users: 64% likely to try out delivery of goods by drone (defines subgroup)
B+10% 7%
22%4%
A
1%4%
13% 3%
5%14%
8%2%0% 1%
1% 5%
71%
29%
64 A study on the societal acceptance of Urban Air Mobility in Europe
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Figure 33: Participants feel more comfortable with manned than with unmanned aircraft systems
Respondents feel safer as a pedestrian with manned air taxis than with delivery drones
Level of comfort with manned & unmanned aircraft systems
Source: EASA UAM societal acceptance survey questions B3. Drones intended for the delivery of goods are remotely piloted aircraft systems with no pilots on board. Assume that they have an average wingspan of 3 metres, would fly at between 120 and 150 metres altitude, and are certified by competent authorities. Please rate how much you agree or disagree with the following statement. C4. Recent studies extend the prospect of aircraft soon transporting passengers, either with a pilot on board or with a remote pilot. You will now see several statements that people might make about such air taxis. Assuming that all of the aircraft are certified by competent authorities, please rate how much you agree or disagree with each statement for each type of air taxi.
30%
32%
23%
29%
23%
20%
27%
16%
30%
14%
7%
10%
5%
15%
6%
21%
8% 13%10%
15%
21%11%12%
20%
10%7%9%
21%16%20%
56%44%
70%30%
44%56%
Strongly disagree AgreeDisagree Somewhat disagree Strongly agreeSomewhat agree
75%25%
43%57%
As a pedestrian on the ground, I would feel safe with unmanned delivery drones potentially flying above me.
As a pedestrian, I accept the fact that manned air taxis could fly above my head.
As a pedestrian, I accept the fact that unmanned air taxis could fly above my head.
I would be interested in trying out a manned air taxi myself.
I would be interested in trying out an unmanned air taxi myself.
Passenger view
Air taxis
Pedestrian view
Delivery Drones
Air taxis
The qualitative interviews also showed that trust in manned services was higher, and that respondents felt more comfortable with initial manned operations of air taxis.
The high level of comfort of EU citizens with manned air taxis, either as pedestrians on the street or as passengers, may be explained by the fact that the aircraft and their operations may look to non-specialists very similar to traditional aviation vehicles and operations, and by the fact that traditional aviation is perceived as very safe by citizens, at least in Europe.
653.2 Survey results (10 key findings)
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Some sub-groups were more comfortable with unmanned air taxis than others. This is true for: the target group for airport shuttle (+17 percent), digital adopters (+11 percent), potential air taxi users and men (both +10 percent), families and those with higher incomes (both +7 percent) and the younger age group of participants between 18 and 44 years (+6 percent). Those with a lower level of comfort compared to the average were: air taxi user rejecters (-16 percent), digital laggards (-15 percent), women (-10 percent), the older age group between 55 and 75 years (-8 percent) and singles (-6 percent).
In general, participants expressed slightly more concern about unmanned services when they thought of it as passengers (32 percent) than when they thought of it as pedestrians (26 percent), as can be seen in Figure 34. There was only small deviation in cities: citizens in Budapest and Milan felt safer (~ +6 percent) while Hamburg, Oresund and Paris felt less safe compared to average (~ -4 percent).
Figure 34:Pedestrians are slightly more concerned about unmanned operations than passengers
C4. Level of comfort with manned & unmanned vehicles – air taxis (3/5)
Source: EASA UAM societal acceptance survey question C4. Recent studies extend the prospect of aircraft soon transporting passengers, either with a pilot on board or with a remote pilot. You will now see several statements that people might make about such air taxis. Assuming that all of the aircraft are certified by competent authorities, please rate how much you agree or disagree with each statement for each type of air taxi.
Pedestrians are slightly less concerned about unmanned operations than passengers themselves
Total
70%30% 40% 50% 60% 90%80%
Paris
Öresund
Milan
Hamburg
Budapest
Barcelona
Share of respondents that feel safe
PedestriansPassengers
32 %
50%30% 40% 60% 70% 80% 90%
Share of respondents that feel safe
Unmanned Manned
26 %
In addition, the results of each question were compared between the cities.
3.2.2 Strong support for use cases in the public interest
Several survey questions helped to identify use cases that respondents expected to be most useful and these, therefore, will probably receive highest acceptance by citizens.
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3
8
2
15
None1
9
Drone delivery of medical supplies to hospitals
18
Commute from a suburb to the city centre
419
17
18
Drone delivery of groceries and Goods in remote areas
3
4
17
Shuttle service to airport
16Transport of emergency medical personnel
412
Drone delivery of meals in urban areas
Long-distance forwarding of heavy cargo
12
Sightseeing by air taxi
Disaster management using drones
5 9
4
Drone delivery of medical supplies to home
Regional air mobility
14
3
4
Transport of injured person to hospital
95
5
Drone delivery of goods from online shopping in urban area
6
7
7
2Point to point travel within a city
25
4
3
1
2
Drone delivery use cases
Emergency use cases
Passenger transport use cases
36%
28%
18%
24%
23%
25%
18%
10%
5%
5%
6%
8%
41%
41%
Perceived usefulness of UAM use cases, %Ranked #1
Ranked #3
Ranked #2
1
X%
Top 5 use case
Sum
As a first step, each participant’s affinity to the new technologies was assessed. This was intended to help understand their attitude towards innovation in general, as this was assumed to impact their openness towards use cases. Participants were then asked to rank the usefulness of fourteen UAM use cases. In this way, the use cases considered the most important for the population were identified. Participants also received an overview of several conceivable use cases that go beyond those shown in the video.
As illustrated in Figure 35, the use cases related to medical and/or
emergency transport were voted most often amongst the three as most valuable ones and therefore ranked highest: transport of injured persons to hospital (41 percent), drone delivery of medical supplies to hospitals (41 percent), transport of emergency medical personnel (36 percent), and using drones for disaster management (28 percent).
Use cases related to the transport of individual passengers, such as sightseeing by air taxi or a flight from one point in the city to another, were considered to be less useful.
Figure 35: Emergency use cases are expected to be most useful
Medical/emergency uses cases are seen as the most useful, whether consisting in drone delivery of medical equipment or urgent transport of persons.
673.2 Survey results (10 key findings)
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These figures indicate that use cases that are in general public interest, notably in the health and safety domains, would be better accepted than use cases fulfilling private and individual needs.
These results from the quantitative survey were confirmed by the qualitative interviews. Interviewees on local, national, and European level saw benefits in UAM preferably when it contributes to public services and interest. Those use cases expected to be most beneficial are linked to emergency and medical transport.
The qualitative interviews, however, also indicated that the emergency or medical character of an operation would not justify any deviation from the safety or security standards. In contrast, higher noise levels could be acceptable if the number of operations for emergency purposes was limited.
3.2.3 Top 3 expected benefits: faster, cleaner, extended connectivity
Through a multiple-select question, survey participants were asked to select up to three benefits and opportunities that the development of UAM could bring for the EU and its citizens. A list of seven possible selections was given (see question A4). This list was not use-case specific. Additionally, participants were invited to name up to three more benefits that did not appear in the survey and may not have been covered by the literature so far.
Again, the use of UAM in emergency situations was perceived to generate the greatest added value: 71 percent of participants expected an improved response time in case of an emergency (see Figure 36). It was also found that, compared to other participants, the age group between 55 and 75 years perceived this to be a more significant advantage on average.
The reduction of traffic jams ranked second (51 percent) on average, closely followed by an expected reduction of
local emissions (48 percent). Better connection to remote areas (41 percent), and the creation of new jobs (32 percent) represented other perceived benefits. The latter was on average more often mentioned as an expected benefit by people younger than 24 years and participants in Barcelona expected this advantage more often than the average respondent (+9 percent). No additional benefits were proposed by respondents in the open question.
Passenger transport: high interest but less useful Qualitative interviews showed that the main reason is the assumption that will only be accessible by a few and probably much more expensive than other modes of transport.
68
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Figure 36: Improved response time is clearly perceived as top benefit
Perceived UAM benefits
Improved response time is clearly perceived as top benefit with all cities ranking it first
0% 10%
Creation of new jobs
30%20%
Development of remote areas
40% 50% 60%
Improved emergency response time
70%
Reduction of traffic jams
80%
None
Market-leading position for Europe
Reduction of local emissions
Share of respondents that selected benefit out of 3 possible
BarcelonaTotal Budapest MilanHamburg Öresund Paris
• Similar perception in all cities as suggested by low spread and steepness of trend curve
• Hamburg and Öresundwith similar opinions
• Reduction of local emissions with highest spread between Paris (37%, -11%) on lower and Budapest (56%, +8%) and Milan (55%, +7%) on upper end
• Creation of new jobs more important in Barcelona (41%, +9%)
(absolute %, +/- difference to avg % in total)
Source: EASA UAM societal acceptance survey questions A4. What benefits and opportunities can the development of urban air mobility bring for the EU and EU citizens? Please select up to 3 answers.
Overall, the replies indicated that participants attach a higher value to perceived positive impacts of UAM on their own security, health, and quality of life, than to other potential benefits – notably the economic ones.
These results from the quantitative survey were confirmed by the qualitative interviews. Interviewees saw and preferred benefits in UAM when it was perceived to be contributed to public services/public interest, such as emergency and medical transport. Connecting remote areas or areas that are currently not sufficiently connected by ground transport systems was also part of this public interest.
Most interviewees spontaneously and immediately referred to the reduction of congestion and emissions as a key benefit, but also at the same time requested that UAM would be integrated into local strategies on multi-modality.
Some of the interviewees considered UAM to have the potential to reduce noise in the city, for instance by replacing the noise of ground ambulances or of some helicopter flights. UAM was also perceived to potentially help citizens gain back green areas, as a result of ‘moving’ the traffic into the air and building less ground infrastructures (roads, bridges, tunnels, etc.). Finally, UAM was seen as a showcase for innovation and for the transition to smart and green mobility.
693.2 Survey results (10 key findings)
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3.2.4 Top 3 concerns: safety, environment/noise and security
Through a multiple-select question, participants were invited to rank the six most important concerns, in their view, regarding the operation of drones in an urban environment, out of a list of nine potential negative effects (see Appendix). This indicative list of concerns was derived from the literature review. Participants were also free to add their own concerns in a free text field.
To avoid confusion, the notions of safety and security were clarified in the questions: safety mainly referred to incidents resulting from a technical or human failure, while security related to incidents caused by harmful deliberate or intentional actions, such as cyber-attacks or the failure of mobile networks. The qualitative interviews confirmed that participants indeed see a clear difference in safety and security, although the translation in some European languages leads to the same wording (e.g. in Hungarian). Individually-stated definitions by participants were very close to each other and in line with the understanding and description in the quantitative survey. Noise was listed as a distinct concern, although it is sometimes included under the wide category of ‘environmental concerns’ in the reviewed literature. The reading of the results should take this element into account, for
instance by adding or not the noise results to the results on environmental impact.
The overall results indicated that safety, security, and environmental issues were the top concerns of respondents – as can be seen in Figure 37, with noise ranking second for air taxis. The results showed that these concerns increase with age, education, and income. Four percent of the respondents expressed no concerns. Participants also had the option to add concerns in a free text field. However, no significant mentions were added.
The ranking of concerns for drones and air taxis was relatively similar – only two primary differences can be seen: • Noise in the case of air taxis ranked second, but was significantly lower for drones coming in 6th place; • Security ranked 10 percent higher for drones. Most likely, this is because drones are unmanned while air taxis are
expected to be manned initially and thus might be less prone to security threats in the eyes of the general public. It can be assumed that air taxis operated by remote pilots or flying autonomously would increase the public concern for safety. In general, slightly more than half of the participants trusted security and cyber-security regarding drones, with men demonstrating more trust than women (+7 percent). See details in Appendix.
Concerns regarding drones
The main concerns for drones also related to safety, security, and environmental impact. Relative to the results for air taxis, the security threat was perceived to be around 10 percentage points higher, which might correlate with the fact that in the scope of this study air taxis were framed to be piloted for initial operation. In addition to Figure 38 it is worth mentioning that a notable demographic difference showed up: concerns related to safety and security increased with the age of participants, while privacy concerns decreased with age.
Concerns regarding the local environmental impact (named by 28 percent as a top-three concern) ranked much higher than those linked to the global environment. Younger participants expressed more concerns regarding the global environment than older respondents. Participants who feared job losses were most likely to be participants with lower incomes and level of education.
Survey results: Concerns on drones and air taxis are nearly the sameDrones
• safety (44 percent)
• security (39 percent)
• environment (36 percent)
Air Taxis
• environment (38 percent)
• safety (37 percent)
• security (29 precent)
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Security5
19%
36%Local2 & global3
environmental concern
28%Noise related concerns4
44%Safety
39%
30%Privacy
30%Job loss
21%Affordability
Visual pollution
4%None
38%
38%
4%
16%
37%
29%
19%
16%
16%
Part of trade-off analysis (conjoint)Concerns regarding delivery drones and air taxis ranked by % of respondents under top 3
1. Incident due to technical or human failure2. Local environmental impact includes air pollution, negative impact on bird life and insects, decreasing biodiversity 3. Global environmental impact covers climate change 4. Covers noise pollution for delivery drones, and noise related to flying aircraft and noise related to vertiports for air taxis 5. Incident due to deliberate harmful action, e.g. by criminal organization or terrorists
Delivery drones Air taxis
Source: EASA UAM societal acceptance survey questions B4. What are you most concerned about regarding drone delivery, both fo r the delivery of day-to-day goods as well as medical supplies? Please consider your own usage of such a service as well as other people using it (e.g. your family or neighbours), which may affect you as well. Please select up to 6 answers. B5. Please sort your main concerns (selected in B4.) from ‘most concerning’ to ‘least concerning’.
1. Incident due to technical or human failure 2. Incident due to deliberate harmful action, e.g. by criminal organization or terrorists 3. Local environ-mental impact includes air pollution, negative impact on bird life and insects, decreasing biodiversity 4. Global environmental impact covers climate change 5. Share of respondents that ranked any environmental concern among top 3 answers
Concerns on drone delivery use cases ranked by % of respondents under top 3
9
Global environmental impact4
Privacy
9
None
13
10
Safety1 22
17 12
9
5
12
11
9
7
Local environmental impact3
7
Security2
Other
13Job loss
89
8
117Noise pollution
76Affordability
6Visual pollution
455
4
9
Ranked #1
Ranked #2
Ranked #3
44%
39%
1%
21%
30%
30%
28%
28%
19%
14%
36%5
X% Sum
Figure 37: Respondents had similar concerns about delivery drones and air taxis
Figure 38: Safety is the leading concerns for drone delivery
713.2 Survey results (10 key findings)
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Concerns regarding air taxis
Additional concerns were considered in the ranking question for air taxis, including those around the infrastructure that enables air taxis to take-off and land, called vertiports. The main concerns related to air taxis as perceived by respondents were noise and environmental impact (both 38 percent, when combining mentions on air taxis and vertiports for the category of noise). These concerns were followed by safety (37 percent), security (29 percent) and privacy (19 percent), as can be seen in Figure 39. Job losses, affordability, and visual pollution (all 16 percent) were raised as well but appear to have a much lower importance to survey participants. Again, demographical differences can be found: concerns around safety and noise related to flying aircraft slightly increase with age, education, and income. Fear of job losses on the other hand decreased with age, education, and income. The results also show that women expressed slightly more concern around environmental issues. A large share of respondents (81 percent) assumed that shuttle services would only be accessible to a few – this is likely to be one of the highest barriers to societal acceptance of air taxis.
Concerns on air taxi use cases ranked by % of respondents under top 3
Source: EASA UAM societal acceptance survey questions C5. What are you most concerned about with respect to air taxis? Please consider your own usage of such a service as well as other people using it (e.g. your family or neighbours), which may affect you as well. Please select up to 6 answers. C6. Please sort your main concerns from ‘most concerning’ to ‘least concerning’.
1. Incident due to technical or human failure 2. Incident due to deliberate harmful action, e.g. by criminal organization or terrorists 3. Local environmental impact includes air pollution, negative impact on bird life and insects, decreasing biodiversity 4. Global environmental impact covers climate change 5. Share of respondents that ranked any environmental concern among top 3 answers 6. Share of respondents that ranked any noise related concern among top 3 answers
37%
29%
5%
9%
15%
18%
27%
16%
16%
12%
29%
19%
16%
15%
0%
10%
38%6
38%5
19
13
8
10
6
7
7
5
4
5
3
3
2
3
1
4
10
10
10
9
7
7
5
5
5
5
5
4
4
3
2
8
7
10
7
6
5
5
6
6
6
7
4
4
4
2
Safety
Local environmental impact
Noise related to flying aircraft
Inner-city space occupation
Security
Privacy
Job loss
Global environmental impact
Affordability
Visual pollution
Squandering of public money
Noise related to vertiports
Flight shame
Downwash
Additional traffic from/to vertiports
Other
None
Ranked #1
Ranked #2
Ranked #3
X% Sum
Figure 39: Noise produced by air taxis is expected to be much higher than that produced by drones
The qualitative interview revealed that noise was the concern most mentioned by stakeholders as they expected noise to be the greatest reason for citizens to complain. Security was also mentioned frequently as a major concern, related for instance to the risk of hacking into the control link and equipping drones with dangerous or explosive goods.
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Respondents often took for granted that safety would be guaranteed by authorities that authorise them to fly. Therefore, safety was not mentioned very often as a key concern. Privacy, such as potentially taking pictures of private areas was frequently mentioned in qualitative interviews, but not in the quantitative survey. In this regard, harassment was mentioned as a potential concern from drones operated by private users (e.g. stalking), particularly if more flights take place in densely populated areas.
Stakeholders also mentioned in interviews additional concerns that had not ranked high in the quantitative survey: insufficient public acceptance, problems of integration of airspace between drones and aircraft, lack of space availability for vertiports and integration into cityscape as well as insufficient integration into the existing transport ecosystem of the city (i.e. UAM just adding another layer of transport congestion, “moving traffic jams into the air”). Some respondents were also concerned by: the affordability of the services, the pressure on the electricity demand, the energy efficiency of transporting through the air, the visual impact on of cultural heritage in old European cities (visual pollution of flights and ground infrastructure), the compatibility of UAM with the “slow mobility” concepts adopted by more and more European cities. Finally, local stakeholders, in particular local authorities, were concerned by a potential lack of involvement in decision making and deployment of UAM in their city/region, especially with regard to topics that impact the local city, such as definition of routes and traffic frequencies.
3.2.5 Safety: existing aviation safety levels are the benchmark
With the help of a comprehensive survey-based trade-off analysis (conjoint methodology as explained in chapter 3.1), the relative importance of different levels of safety, noise, and visual pollution on the acceptance level of the survey respondents was assessed. While these results can give an indication of how different improvements in safety, noise and visual pollution are perceived by the public – and what measures could enhance public acceptance – the analysis cannot directly indicate precise regulatory measures, which must also be influenced by other considerations. One reason for this is that the perception of the safety dimension might be underrated in this survey as people are used to and expect high safety standards when it comes to aircraft. This particular fact was highlighted many times in the qualitative interviews performed after the quantitative survey.
A separate analysis was done for drones and for manned air taxis as different responses were expected due to the different size and application of the aircraft. The details are shown in the following differentiated results both for drones and air taxis.
Detailed trade-off analysis for drones
When it comes to delivery drones, 24 percent of participants indicated that they would accept delivery drones in any presented scenario, given the worst safety, noise, and visual pollution level. In contrast, one out of five participants (20 percent) indicated they would not accept delivery drones at all, despite the best level of safety, noise, and visual pollution (see Figure 40).
The results from this simplified trade-off analysis indicated that citizens’ acceptance could be improved by 56 percent by implementing the highest levels for the safety, noise and visual pollution dimensions. Within these 56 percent, the safety dimension is the main influencing factor (increasing acceptance by 23 percent). The results for delivery drones imply that a change of safety from level 1 (one drone has five times the likelihood of hitting a pedestrian as one car) to level 2 (one drone has the same likelihood of hitting a pedestrian as one car) could increase the public acceptance by 12 percent, which is also the highest incremental increase for all three dimensions and almost as much as the complete visual-pollution dimension. A change in the safety dimension from level 2 to level 3 (one drone has 1/10th the likelihood
733.2 Survey results (10 key findings)
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Source: EASA UAM societal acceptance survey questions B7. Put yourself in the year 2030: drones with about 3-metre wingspans, certified by competent authorities, are flying at altitudes of up to 150 metres. In the following section, you will be asked which scenario out of three alternatives is most acceptable from your perspective. Please choose your most preferred option out of the three alternatives shown. B8. Again, put yourself in the year 2030. How acceptable would you find the following scenarios for the future? Please rate each scenario based on the scale shown below.
1. Figures may be used to assess different scenarios for regulation; however, survey participants are not expert in regulation efforts and may have misleading expectations (too low and too high); answers are always a snapshot
80
24
Accept delivery
drones in any form
5
7
Safety
5
9
7
Visual pollution
20
3
Accept no delivery
drones at all
13
Would accept
best case scenario
Total
3
Noise
4
12
10023
20
56%
Secured acceptance Increase in accepancefrom level 3 to 4
Influenceable range by settingattributes from level 1 to 4
Increase in accepancefrom level 1 to 2
Increase in accepancefrom level 2 to 3
Non-addressable acceptance
SumExample: Levels regarding safety standard for commercial drones: different acceptance rates of up to 80% may be achieved
Goo
dBa
d Level 1: Ø utility = -2.31 One drone has 5x the likelihood of hitting a pedestrian as one car
Level 4: Ø utility = 1.51One drone has 1/100th the likelihood of hitting a pedestrian as one car
Level 2: Ø utility = -0.06One drone has the same likelihood of hitting a pedestrian as one car
Level 3: Ø utility = 0.86One drone has 1/10th the likelihood of hitting a pedestrian as one car
A
A
Figure 40: Trade-off analysis results for drones
of hitting a pedestrian as one car), or from level 3 to level 4 (one drone has 1/100th the likelihood of hitting a pedestrian as one car) could increase societal acceptance by 5 percent each.
Detailed trade-off analysis for air taxis
Taking a closer look at air taxis (see Figure 41), the maximum achievable acceptance rate is 82 percent, as 18 percent of respondents said they would never accept an air taxi as part of urban transport system even if the best level of safety, noise and visual pollution could be achieved. However, nearly one third indicated that they would accept air taxis in any presented scenario, given the worst safety, noise and visual pollution level. This means the range of influence by different levels for the dimensions of safety, noise and visual pollution is 49 percent in the given simplified scenario.
Within these 49 percent, the noise dimension is the main influencing factor (increasing acceptance by 18 percent), followed by safety (16 percent) and visual pollution with (15 percent). In general, as well as for drones, visual pollution is the least important factor for citizens. Noise on the other hand moved to the first place for air taxis. The results for air taxis imply that a change of safety from level 1 (~5 fatalities per 10^9 PAX km) to level 2 (~2 fatalities per 10^9 PAX km) could increase the public acceptance by 8 percent. A change in the safety dimension from level 2 to level 3 (~0.05 fatalities
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per 10^9 PAX km), or from level 3 to level 4 (~0.01 fatalities per 10^9 PAX km) could increase societal acceptance by ~5 percent each. A reason why noise is perceived more important in relation to safety for air taxis could on the one hand be that vehicles are larger and thus more associated in respondents’ minds to today’s helicopters flying in cities, which are very noisy, and on the other hand that the safety of currently flying vehicles is already perceived as very high and thus is not a top concern for most citizens. The results on safety imply that a change of noise from level 1 (one air taxi is as loud as a leaf blower) to level 2 (one air taxi is as loud as a truck driving by at city speed) could increase the public acceptance by 5 percent. A change in the noise dimension from level 2 to level 3 (one air taxi is as loud as a car driving by at city speed), or from level 3 to level 4 (one air taxi is as loud as a bicycle riding by at city speed) could increase societal acceptance by 6-7 percent each.
Trade-off analysis via conjoint question for air taxis: different acceptance rates of up to 82% may be achieved, but results are less differentiated
Source: EASA UAM societal acceptance survey questions C7. Put yourself in the year 2030: air taxis with wingspans of up to 12 metres, certified by competent authorities, are flying at altitudes of about 150 metres. In the following section, you will be asked which scenario out of three alternatives is most acceptable from your perspective. Please choose your most preferred option out of the three alternatives shown. C8. Again, put yourself in the year 2030. How acceptable would you find the following scenarios for the future? Please rate each scenario, based on the scale shown below.
65
Noise
4
82%
7
8
Accept no delivery
drones at all
8
Visual pollution Would accept best
case scenario
100
15
Total
33
4
Accept delivery drones in any
form
18
5
Safety
16%
18 3
49%
Secured acceptance
Influenceable range by settingattributes from level 1 to 4
Increase in accepancefrom level 1 to 2
Increase in accepancefrom level 2 to 3
Increase in accepancefrom level 3 to 4
Non-addressable acceptance
Sum
A
A
A
Figure 41: Trade-off analysis results for air taxis
3.2.6 Environment: priority is protection of wildlife
As indicated above, environmental impact was the second highest concern of citizens. In order to get a better understanding of the exact nature of this concern, the quantitative survey invited participants to separately rank detailed environmental concerns they have with regard to drones and air taxis. They were given a list of seven concerns each to rank from “most concerning” to “least concerning”. Also, they had the chance to choose “none of these”.
As the results in Figure 42 show, the top-three environmental risks that survey participants expected to see with delivery drones and air taxis were the same: nearly two out of three participants feared a negative impact on animals from drones, while 56 percent feared the same for air taxis. Negative impact on animals was more often mentioned as a concern by the age group older than 65. This age
The main environmental concerns relate to the risk of negative impact on wildlife (birds, insects, bats, etc). The results are identical across the six cities.
753.2 Survey results (10 key findings)
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group was concerned, for example, that animals could be disturbed and that this could lead to a reduction of birds in urban areas. Noise pollution is also an environmental concern: for both delivery drones and air taxis more than half of participants named noise as one of their top-three-concerns. The third concern related to the environmental and
climate impact of the manufacturing and production of the vehicles and of their batteries.
Environmental concerns on drones
When taking a closer look at the expected environmental impact of drones, three concerns clearly stood out: negative impact on animals (62 percent), noise pollution (52 percent) and environment and climate impact from production including batteries (43 percent). This last concern was significantly higher than climate impact from operation, with only one third of participants being concerned about the latter.
Source: EASA UAM societal acceptance survey questions B9. What are your greatest concerns when it comes to the possible envir onmental consequences of drone delivery? Please sort the following answers from 1 being ‘most concerning’ to 7 being ‘least concerning’ or select ‘none of these’. C9. What are your greatest concerns when it comes to the possible environmental consequences of air taxis? Please sort the following answers from 1 being ‘most concerning’ to 7 being ‘least concerning’ or select ‘none of these’.
Environmental concerns ranked by % of respondents under top 3%
Delivery drones Air taxis
Ranked under top 3
62
52
43
33
29
28
28
8
Emissions
Negative impact on animals
Noise pollution
Sealed surfaces
Environmental and climate impact from production (incl. battery production)Climate impact from operation (incl. energy expenditure)
None
Environmental impact from disposal
56
53
42
40
30
33
31
5
Figure 42: Details on environmental concerns
At the end of the list, participants ranked lowest the concerns around disposal, sealed surfaces, and emissions (~28 percent). The concern about emissions decreased by age: the age group 55 to 65 years expressed less concern on this topic (-8 percent), while the youngest age group 18 to 25 year expressed higher concern (+12 percent).
The results demonstrate an amazing homogeneity across the cities, with responses and the rankings closely aligned when it comes to environmental concerns on drones (see Figure 43). It is to be noted however, that the largest differences in concerns between cities relate to the environmental impact of local emissions, likely driven by differences in perceived current air quality in the respective cities.
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Concerns for delivery drones
Source: EASA UAM societal acceptance survey questions B4. What are you most concerned about regarding drone delivery, both for the delivery of day-to-day goods as well as medical supplies? Please consider your own usage of such a service as well as other people using it (e.g. your family or neighbors), which may affect you as well. Please select up to 6 answers. B5. Please sort your main concerns (selected in B4.) from ‘most concerning’ to ‘least concerning’.
40%0% 10% 30%20%
2.5
50%
1.5
60% 70%
Climate impact from operation (incl. energy expenditure)
7.5
0.5
None
Emissions
Sealed surfaces
Noise pollution
3.5
4.5
Environmental impact from disposal
5.5
Environmental and climate impactfrom production (incl. battery production)
6.5
Negative impact on animals
8.5
Share of respondents that rank concern under top 3
BarcelonaTotal Budapest Hamburg Milan Öresund Paris
Cities very aligned in opinion, especially for highly ranked environmental concerns
Figure 43: There is a major difference between cities on concerns around delivery drones and emissions
Environmental concerns on air taxis
When it comes to air taxis, two concerns ranked significantly higher than others: the negative impact on animals (56 percent) and noise pollution (53 percent). Again, environmental and climate impact for production (incl. battery) and operations ranked third and fourth, but these concerns were mentioned nearly the same number of times (42 percent for air taxis vs. 40 percent for drones). While younger people were more concerned with climate change, they were less concerned by noise (-10 percent).
77
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Environmental concerns for air-taxis
Source: EASA UAM societal acceptance survey questions B4. What are you most concerned about regarding drone delivery, both for the delivery of day-to-day goods as well as medical supplies? Please consider your own usage of such a service as well as other people using it (e.g. your family or neighbors), which may affect you as well. Please select up to 6 answers. B5. Please sort your main concerns (selected in B4.) from ‘most concerning’ to ‘least concerning’.
1.5
Environmental and climate impactfrom production (incl. battery production)
0% 10%
4.5
30%
Environmental impact from disposal
20% 40% 50% 60% 70%
0.5
Noise pollution
None
Emissions
2.5
Sealed surfaces
3.5
Climate impact from operation (incl. energy expenditure)
5.5
6.5
7.5
Negative impact on animals
8.5
Share of respondents that rank concern under top 3
Total BudapestBarcelona Hamburg ÖresundMilan Paris
Impact on animals and noise are highest ranked environmental concerns regarding air taxi use case
Figure 44: Concerns on air taxis are nearly the same across survey cities
Just as for drones, participants were aligned across geographies on the top environmental concerns. However, the largest spread in opinion across cities showed up when considering environmental impacts both from disposal of air taxis and emissions (see Figure 44).
The risk to wildlife was repeated during the qualitative interviews, principally by local stakeholders. Examples mentioned were birds disturbed by noise, or bats and other animals disturbed by lights if aircraft was flying at night.
Possible mitigation option: the eco-label
As part of understanding potential solutions to societal acceptance and buy-in in relation to environmental impact, the questionnaire invited respondents to indicate whether, in their view, the environmental impact of Urban Air Mobility operations should be evaluated by the authorities and made public, e.g. via an eco-label, such as the one being developed by EASA for traditional aviation:
Figure 45
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As illustrated by Figure 45, a large majority (74 percent) of survey participants saw advantages in introducing an eco-label for commercial Urban Air Mobility vehicles and operations.
Source: EASA UAM societal acceptance survey question D4. Should the environmental impact of urban air mobility operations be evaluated by the authorities and made public, e.g., via an eco-label such as the one shown below (picture included in back-up)? Please select one answer.
A vast majority of 74% in favor for introducing an eco -label for commercial VTOL
23% 74%Total 3%
No Maybe, don’t know Yes, certainly
Figure 45: An eco-label for commercial VTOL can increase societal acceptance
Again, the responses were similar across all surveyed cities. Participants from Barcelona showed a slightly more positive attitude towards such an eco-label (+8 percent) compared to others. Oresund (-8 percent) showed the lowest enthusiasm.
3.2.7 Noise: acceptable at level of familiar city sounds
The findings of the noise perception study described in the survey methodology section (see § 3.1.3) are presented in this section.
Figure 46 below presents a summary of the results in which the average ratings as well as the minimum and maximum ratings are shown. It must be noted that the worst ranking level was largely driven by two listeners, as their responses to most sources appears to have been much more adverse than the responses from others. These differences in responses were not due to any difference in the test methodology, such as listening at different sound levels: the conditions were identical for each listener.
The volume values presented in dBA units in Figure 47 below correspond to maximum A-weighted noise levels and are not integrated over time. Consequently, they correspond to the loudest instant in the sound sample, but do not capture that noise events may be significantly different in duration from one another, and
thus result in very different perceptions by the listeners. This aspect should be accounted for and soften the following conclusions.
Citizens’ acceptance of noise expected to increase over timeAt the same sound level, participants of a noise test felt more annoyed by UAMvehicle sounds than by sounds that theyalready know.
Therefore, it can be expected that thelevel of annoyance will decrease as citizens become familiar with sounds of UAM vehicles, and provided that the sound level and character.
793.2 Survey results (10 key findings)
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53
Sample size n=20
109876543210
AverageHow annoying sound was perceived
Sound type VolumeExtremely annoyingNot at all annoying
1. UAM noise is more annoying at same noise level…
Helicopter 80 dBA
Aircraft 80 dBA
Motorbike 80 dBA
80 dBABus
Light Drone 80 dBA
Large Drone 80 dBA
Air Taxi 1 80 dBA
Air Taxi 2, Position 1 80 dBA
70 dBAAir Taxi 2, Position 2
60 dBAAir Taxi 2, Position 3
56
…
AverageAnnoyance
Extremely annoyingNot at all annoying
109876543210Sound type
Air Taxi 1
Large Drone
Light Drone
Air Taxi 2 Position 1
Helicopter
Motorbike
Bus
Aircraft
Air Taxi 2 Position 3
Air Taxi 2 Position 2
@VG: formtemplate ofpage
Figure 46: Result overview of noise perception study
Figure 47: Statistical analysis of responses
Although this is a small sample of results, the responses have also been analysed statistically. Figure 47 below shows mean response and the 95 percent confidence intervals for each source. A repeated measures ANOVA (analysis of variance) shows that there is a statistically significant effect of vehicle type on annoyance (F(9, 180) = 24.17, p < 0.001).
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Some important information can be inferred from Figure 46 and Figure 47. Figure 48 shows the comparison between familiar sounds and UAM aircraft played at same maximum noise level. Figure 46 shows the noise perception rating for sources at different distances.
Observations of sounds played at the same noise level (see Figure 48):
• Responses showed a clear separation between the drone/air taxi sources and the others, at the same sound level; • This separation is particularly marked between the drone/air taxis and the road vehicles;• The air taxi and large drone scored the highest mean average result of 7.8. The synthesised air taxi at 80dBLAmax,F
(Air Taxi Position 1) scored only slightly lower (mean of 7.7) but had the most instances of the highest score of 10 (5 times).
This could lead to conclusions that unfamiliar sounds, in this case UAM sounds, are perceived more negatively or that the sound characteristics of these aircraft lead to a more negative rating at the same maximum noise level compared to the other sounds to which the participants were exposed.
54
Sample size n=20
AverageHow annoying sound was perceived
Sound type VolumeExtremely annoyingNot at all annoying
Known Sounds
UAM Sounds
1. UAM noise is more annoying at same noise level…
Helicopter 80 dBA
Aircraft 80 dBA
Motorbike 80 dBA
80 dBABus
Light Drone 80 dBA
Large Drone 80 dBA
Air Taxi 1 80 dBA
Air Taxi 2 80 dBA
109876543210
@VG: formtemplate ofpage
Figure 48: Result comparison between familiar sound and Urban Air Mobility vehicle sounds at same maximum noise level
813.2 Survey results (10 key findings)
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Observations with respect to different distances (see Figure 49):
• As expected, the synthesised air taxi score dropped with distance/sound level, both in terms of individual ratings and the mean average. The difference in responses between Position 2 and Position 3 was greater than the difference in rating between Positions 1 and 2;
• As expected, the synthesised air taxi at the furthest distance (and 20dB quieter than the other sounds) scored the lowest ranking, with an average score of 3.2;
• It can be seen that at a distance equivalent to 60dBLAmax,F the annoyance level was below the annoyance for the familiar reference sounds at 80dBLAmax,F. The level of 60dBLAmax,F on top of a background noise of 55dBLAeq seemed to be largely acceptable for the 20 test participants.
55
AverageHow annoying sound was perceived
Sound type VolumeExtremely annoyingNot at all annoying
2. Annoyance levels significantly decline with noise levels
Air Taxi 2, Position 1 80 dBA
70 dBAAir Taxi 2, Position 2
60 dBAAir Taxi 2, Position 3
109876543210
@VG: formtemplate ofpage
Figure 49: Noise perception at different distances
Additional response comments
The following summarises the main themes that were noted in the listeners’ comments or mentioned after the listening tests to the Arup staff: • Familiarity: Most comments featured recognition of or familiarity with the sounds being linked to lower (relative)
annoyance scores (e.g. the aircraft and motorbike), with one commenting that these sounds were ‘part of everyday life’. One listener linked the familiarity of the sound with perceived frequency of occurrence, i.e. ‘I only hear an aircraft a few times a day, so I don’t find the sound too annoying’. Similarly, some respondents said they owned a motorbike so were not annoyed by that sound.
• Unfamiliarity: In several cases participants did not understand what they were listening to or could not imagine the source of the noise sample, as evidenced by descriptors including ‘lawnmower’,‘swarm of bees’or ‘boat’. The listeners had not been asked to try to identify the sources but several attempted to do so.
• A difference in response between familiar and unfamiliar sounds was also reflected in the response ratings, shown by Figure 49. No visualisation was provided during the tests that could have helped people understand the sound sources. It is not known whether this had a positive or negative effect on responses to UAM sounds.
• The relationship between familiarity and response should be investigated in any future tests, since it may be that responses would become more aligned with current transportation sources if such aircraft were to become a common feature of the urban soundscape.
• Whilst not intentionally part of the pilot study, the speed of the pass-by was also commented on by several participants. i.e. a slow pass by was more annoying than a quick one, possibly because of the amount of time exposed to the Lmax level or because the LAeq would have been higher for the longer exposure. This could be investigated further in a larger study, to consider the relationship with rise-time and startle, as well as duration of the sound.
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Sound character and response
Spectrograms have been calculated for each sound source, to enable a preliminary assessment of the effect of sound characteristics on responses and show the change in the magnitude and frequency content of the sound with time. Examples are shown below. Time is plotted along the horizontal axis and frequency on the vertical axis. The colours indicate the sound pressure level: blues are the lower levels, with increasing levels being reflected in magenta, through red to white.
Air taxi
Large drone
Helicopter
Bus
833.2 Survey results (10 key findings)
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There are elements of tonality at frequencies where they might be expected, such as low frequencies for the bus. There is generally a clear tonal component at around 3kHz for most drones/air taxis, which would correspond to frequencies where humans are most sensitive. However, the tonality is much more distinct with the air taxis than with the drones, which may be a factor in their higher annoyance rating.
The drones and helicopter exhibit a series of vertical transients with higher magnitude darker lines occurring in quick succession in their spectrograms. This characteristic is not evident in the other samples.
Conclusions of noise study
Initially it must be emphasised that this study was only meant to provide initial takeaways and insights. Results are not sufficiently significant and would have to be done at larger scale to draw conclusions. Nevertheless, the results provide insights that can support the design of further and more detailed studies.
It is apparent from this small study that there was a difference between reaction to sounds of drones/air taxis (at the same sound level) and reaction to other sound sources. This may be due to differences in sound character or differences in familiarity with the sounds. Participants more easily accepted sounds they were familiar with (even helicopters or motorbikes) and expressed annoyance towards unfamiliar sounds at the same maximum noise level.
This conclusion must be weighed alongside the fact that, despite being played at the same maximum noise levels, the noise samples exhibited very different time durations or rise-and-fall dynamics. This introduces a clear bias in the perception, as longer noise events are always perceived more negatively than shorter ones.
This consideration would benefit from further investigation through a larger set of tests. It may follow that sound level limits for these types of aircraft may need to be assessed and treated differently to other sources. Conversely, increased familiarity with such sound may lead to a greater acceptability in future.
A larger study would also allow the relationship between annoyance and other acoustic characteristics to be investigated.
As expected, people reported lower annoyance to sounds at lower sound levels. A statistically significant sample to test this in relation to unfamiliar sounds would be a beneficial part of a future study. For this pilot study, the tests adjusted only the level across three different sound levels, and not any other acoustic characteristics that would arise from the same source being at increasing distances: at a greater distance, the same noise source is attenuated, but its rise-and-fall dynamic (perceived duration) can increase.
The duration for which the sound was at a high level appears to have affected the responses. This should be assessed further to establish whether there is any relation between the duration of the sound and potential ‘startle’ effects of steep rise times. These issues could be particularly important when defining and assessing the potential locations of vertiports.
Further consideration should be given to whether visual representation of the sound sources should be included in any future wider study and how this might affect the outcomes.
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3.2.8 Security: need to build confidence and trust in citizens
As indicated by the results to the generic concerns questions, security was the third highest concern of respondents. The survey looked into more detail at the trust level of citizens regarding the security and cyber-security of UAM, both for drones and air taxis. Participants were invited to indicate to what extent they trust that advanced aircraft flying in an urban environment will be technologically secure and protected against malicious threats and actions. The participants could then indicate different levels of trust ranging from fully trust to fully mistrust.
The results can be seen in Figure 50: The level of trust for delivery drones as well as for air taxis is just above 50 percent and therefore could be improved.
Source: EASA UAM societal acceptance survey question D2. To what extent do you trust that advanced aircraft flying in an urban environment will be technologically secure and armed against threats from hackers (such as criminal organisations, hacktivists or terrorists) in the following cases? Please select one answer per row.
23%
25%
12%
12%
10%
10%
31%
32%
19%
17%
4%Delivery drones
3%Passenger Transport
Fully mistrust Somewhat mistrustMistrust Somewhat trust Trust Fully trust
46% 54%
47% 53%
X% Sum
Trust levels show similar trends for drones and passenger transport
Figure 50: Trust levels in VTOL technology incl. security and cybersecurity
A minor difference can be seen between the demographics: while men were more likely to trust UAM services (~+7 percent), women (~-7 percent) and the older age group between 65 and 75 years (~-8 percent) were more likely to show mistrust. It is no surprise that the ones that during the survey answered to be rejecters of delivery and/or air taxi usage as well as digital laggards are amongst those with lowest trust level towards UAM (-16 to -27 percent less trust).
Some small local differences in trust were observable: The highest level of trust showed up in Milan (+10 percent for drones, +7 percent for air taxis) and Budapest (+7 percent, +10 percent). The lowest trust level showed up in Paris (-10 percent, -8 percent).
Another question tested whether the trust level of respondents would increase if the regulators were to develop regulations to manage cybersecurity risks (certification and operation of aerial vehicles). Figure 51 shows that on average 37 percent indicated that cybersecurity regulations would not influence their trust in vehicles. Also it can be seen that the public has no preference if these topics are regulated on European, national or regional level.
853.2 Survey results (10 key findings)
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7%
7%
8%
6%
6%
5%
35%
37%
39%
25%
25%
25%
21%
20%
18%
7%
6%
5%Regional or local authorities
European authorities
National authorities
Decrease a lot
Decrease slightly
Stay the same Increase
Increase slightly Increase a lot
52%
Source: EASA UAM societal acceptance survey question D3. Would your trust increase if the following regulators were to develop regulations to manage cybersecurity risks (certification and operation of aerial vehicles)? Please select one answer per row.
X% Sum
On average, 37% indicate cybersecurity regulations by authorities would not influence their trust
51 %
48 %
14%
13%
12%
Numbers wrong?Figure 51: Impact on trust level due to regulation by authorities
Figure 52: Impact on trust level due to regulation by authorities
On the contrary, the survey results regarding concerns related to vertiports (see below, Figure 53) showed that security issues linked to the vertiport operations are not a major concern of respondents (ranking 5th). This could be explained by the fact that vertiports are still at the conceptual stage and that very few representations are available to the general public at present.
Interview respondents on local and national level indicated that they would prefer a regulation on European level that they can rely on.
3.2.9 Ground infrastructure: must be integrated well
The survey also looked at the attitude of citizens towards emerging UAM ground infrastructure for drones and air taxis.
Infrastructure for delivery drones
To get insights on drone delivery, participants were asked how comfortable they would be with different modes of drone delivery for medium-sized parcels (max. 120 x 60 x 60 cm, up to 5 kg). Overall, participants said that the closer to a private area that a drone can deliver, the more comfortable they would feel (see Figure 52). Some 68 percent would prefer delivery to their own garden or private space, 67 percent delivery to a station within the neighborhood, 52 percent on walkway in front of the house, 45 percent would like a delivery on
Integration into the city and local transport networkUAM services need to be integrated into the existing local mobility system. Visual impact of aircraft and infrastructure should be limited and preserve city landscape.
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the top of the house or to their office. Still 39 percent said they would be satisfied with delivery to a nearby park. The option of delivery to garden was especially popular in Budapest (+16 percent) and Milan (+11 percent) and least popular in Oresund (-13 percent). The option of delivery in front of the house and on the roof of the house were in the midfield – but showed high deviations: The approval was for example higher within the groups of participants aged 25 to 44 (+2 percent for delivery on walkway, +8 percent for delivery on top of house) and families (+7 percent, +6 percent), while it was lower within the older age group aged 55 to 75 (-6 percent, -10 percent) and within the group of singles (-7 percent, -4 percent).
Source: EASA UAM societal acceptance survey question B6. How comfortable would you be with the following modes of drone delivery for medium-sized parcels (max. 120 x 60 x 60 cm, up to 5 kg) at places near your home? Please select one answer in each row.
17%
15%
6%
10%
14%
9%
22%
30%
22%
15%
9%
7%
33%
22%
12%
Garden or private area
18%
14%
13%
23%
21%
5%
24%
28%
Walkway in front of my house
16%
17%
14%
Roof on top of house or office
20%
12%
Nearby park
23%
10%
Delivery station in neighbourhood
Somewhat uncomfortableVery uncomfortable
Somewhat comfortableUncomfortable
Comfortable
Very comfortable
Preferred drop-off locations for parcels delivered by drones are garden / private area and delivery station in neighbourhood
High level of comfort Low level of comfort
68% 67% 52% 45% 39%
X% Sum comfortable
Figure 52: The closer to the house the better is what counts for drone delivery
Infrastructure for air taxis
It is assumed that air taxis will need specific infrastructure on the ground to embark and disembark passengers, as well as to recharge their batteries, as their autonomy will be limited. Assuming that a take-off and landing-station would be close to them, i.e. close to their living or working place (under 50 metres), the respondents to the questionnaire were requested to indicate what they would be most concerned about. They replied that (Figure 53), noise from take-off and landing (48 percent) and safety (41 percent) are their main concerns. In line with the concerns on drones and air taxis in general, the fear of noise pollution increased with age and education. The third concern mentioned most often for vertiports is on visual pollution (32 percent). Participants also feared that vertiports close to where they are living could negatively influence their privacy (31 percent).
873.2 Survey results (10 key findings)
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Concerns regarding vertiports ranked by % of respondents under top 3
48%
41%
14%
28%
31%
32%
31%
29%
15%
Source: EASA UAM societal acceptance survey questions C11. Assuming that a take-off and landing-station is close by (under 50 metres), what are you most concerned about? Please select up to 6 answers. C12. Please sort your main concerns from ‘most concerning’ to ‘least concerning’.
1. Incident due to technical or human failure2. Incident due to deliberate harmful action, e.g. by criminal organization or terrorists
Security2
More people walking by
15
21
17
8
15
More road traffic in neighbourhood
Noise from take-off and landing
11
10
5
8
14
11
Safety1
1213
4
Visual pollution
8Privacy
0
9
5
9
1012
116
8Space occupation needed for living or recreation
6
54Space occupation needed for retail
12
None
Ranked #2Ranked #1 Ranked #3
Figure 53: Concerns related to vertiports
These results are to put in parallel with the notion of “not in my backyard” regularly expressed during the qualitative interviews. UAM was seen as positive and attractive as long as the impact does not affect specifically and negatively an individual or a group of individuals.
3.2.10 Regulatory authorities: must work together at all levels
As one objective of the study was to support future regulatory work on UAM, participants’ expectations towards European, national, regional or local authorities were assessed. The survey polled participants’ trust levels towards local, national and European authorities to handle the risks associated with UAM and to adopt adequate regulations (see questions D1 to D3).
As results in Figure 44 indicate, the participants’ trust towards local, national, regional and European authorities came with nearly the same proportion, with a slightly higher level of trust overall towards European regulatory authorities.
A closer look at the results in each of the six cities reveals local differences. As demonstrated by in Figure 55. participants form Budapest, Italy and Spain expressed better trust towards European authorities, while participants from Germany and Oresund trusts better national and local authorities. It is to be noted that respondents from Paris were the most sceptical and express a low trust level towards all authorities, below 50 percent in all cases.
Qualitative interviews with local stakeholders reinforced this finding. Most of them, except those coming from cities where pilot projects or demonstrators are taking place, had more questions than answers to the interview questions, as UAM is new and information on it has not yet reached the local decision-making level. Many of them are concerned by potentially insufficient information to take informed decisions as
Public acceptance will most likely increase if authorities on all levels work together. This will also allow to link the UAM operations to the different local conditions.
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D1. Trust levels in regulation authorities – per city (2/3)
Source: EASA UAM societal acceptance survey question D1. To what extent do you trust the following authorities to handle the risks and adopt regulations needed to manage urban air mobility (including safety, noise control, environmental protection, security, cybersecurity, etc.)? Please select one answer per row.
Budapest with highest trust in European authorities, Oresund with higher trust in local and national authorities
European authorities
30% 29% 29% 29% 31% 35%27%
21% 21%28%
17%
25% 21%
16%
6% 8%
9%5% 5%
4%
22% 21% 17%26% 22% 22% 21%
11% 12%9%
12%10% 10% 15%
10% 8%
7%
14%
6% 7%
16%
2%
Fully mistrust Somewhat trustMistrust Somewhat mistrust Trust Fully trust
30%25% 28%
34% 31% 35%26%
20%
18%17%
18% 21%
26%
18%
4%
5%5%
7%
4%
22% 23% 18%24% 28%
18% 21%
14%17%
17%12%
10%
8%
18%
11%13%
15% 9% 7%
6%
13%
3%3%
30% 28%33% 35%
27%34%
25%
20%18%
19% 18%
21%
27%
19%
5%
5%
5%
24% 24% 20%26%
32%
20% 23%
13% 14%14%
11%
11%
9%
17%
9% 12%10%
8%7%
5%
11%
4% 4% 2%2%
Total Total TotalNational authorities Regional or local authorities
59%
67%
48%
61% 61%57%
48% 47%50%
55% 55%
68%
54%
48%51%
56% 54%50%
65%
54%
49%
X% Sum (+/- difference to avg % in total)
Reference page
22%
22%
24%
11%
14%
13%
11%
9%
30%
30%
30%
21%
20%
20%
6%
4%
European authorities
National authorities
20
4%Regional or local authorities
TrustFully mistrust Mistrust Somewhat trustSomewhat mistrust Fully trust
43%
46%
46%
57%
54%
54%
Source: EASA UAM societal acceptance survey question D1. To what extent do you trust the following authorities to handle the risks and adopt regulations needed to manage urban air mobility (including safety, noise control, environmental protection, security, cybersecurity, etc.)? Please select one answer per row.
X% Sum (absolute %, +/- difference to avg % in total)
Figure 54: Overall, participants trust European authorities a bit more than national, regional or local ones
Figure 55: Whom citizens trust most depends on where they come from
893.2 Survey results (10 key findings)
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well as on insufficient involvement of local authorities in the deployment of UAM locally. They did not see easily how the role of local authorities would be articulated with that of national and European authorities.
Overall, the study highlighted the expectations by respondents and stakeholders that all levels of authorities play a role in the deployment of UAM. The very specific nature of UAM operations, closely linked to the local conditions, needs and constraints can explain this expectation.
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This chapter considers possible actions and measures that could be taken to address stakeholder concerns and increase social acceptability levels of UAM. Preliminary qualitative assessments of costs and benefits for some measures are considered.
4. Expectations and options for action
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4.1 Citizens’ and stakeholders’ expectations
The cumulated results of the quantitative survey, qualitative interviews and noise simulation reveal some general trends in public acceptance in the EU.
EU citizens initially and spontaneously expressed a positive attitude toward and interest in UAM, seeing it as a new and attractive means of mobility able to offer faster, cleaner and extended connectivity. However, they also expressed significant concerns. When prompted to reflect upon the concrete consequences of potential UAM operations in their city, EU citizens want to limit their own exposure to risks and expect authorities to pro-actively respond to these concerns.
Public acceptance is a critical success factor of the future deployment of UAM, and it must be secured by a number of preventive actions. Some of these actions fall under the competence of regulatory authorities :
• Address safety, ensuring that UAM has a safety level equivalent to that of current aviation operations for passengers and for people on the ground;
• Ensure local environment protection by less local emissions, less congestion and sufficient protection of wild life;• Ensure global environmental protection from a life-cycle point of view; • Ensure birds and insects are not affected by the production of the aircraft and their operations;• Address UAM noise, ensuring that the level, frequency and duration of the related sounds is kept at acceptable levels,
notably when first UAM operations start, as unfamiliar sounds are perceived as more annoying than familiar ones; to this effect, further research should be conducted to confirm the survey results with larger panels;
• Prevent security and cyber security risks, particularly for drones, as manned aircraft are perceived as more secure, probably due to the presence of a pilot onboard;
• Ensure coordinated actions between all authority levels (European, national and local); EU citizens trust them equally and expect all levels to be involved in decision-making. Local authorities expect more information and guidance, and want to be involved at an early stage in the decision-making, concerning the roll out of UAM in their territory. This association will be key for buy-in and acceptance;
• Conduct prior studies, for example measuring local noise and wild-life impact and defining quiet zones and times; this could help reduce affected stakeholders’ uncertainty or fear regarding the introduction of UAM;
• Ensure that UAM fits with the notion of “public interest” by making it affordable to all, and integrating it into the local (multimodal) mobility system/network accessible to all;
• Support the deployment of UAM supported by timely, sufficient and transparent information and dialogue with to citizens and local stakeholders groups;
• Encourage demonstration and pilot projects in order to show that UAM can actually work and is safe. Gradually introducing use cases with the highest benefit for the general public, e.g. transporting medical goods with manned eVTOLs could also reinforce societal acceptance;
• Regulate airspace/aviation and aircraft design dimensions carefully. The integration of airspace should also be clarified, as this can provide a framework for the operation of conventional and UAM aircraft in the same airspace, e.g. around airports.
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4.2 High level cost-benefit analysis of options
Some of these possible actions, notably the first four, fall at least partly under the competence of the EU regulator, in particular EASA.
A qualitative cost benefit analysis was therefore conducted to assess the options available to act on the safety, security, noise and environment dimensions. The following assessment, which was not the core subject of the study, is provided as a high level indication of the options available to EASA to address these four specific concerns.
Possible safety measures
For safety, two different levels were evaluated: A high safety bar for UAM, similar to the one established in commercial aviation today and a slightly lower safety bar similar to the highest requirements in the automotive industry.
If the high safety bar were adopted for UAM, it could lead to significant higher costs for the business cases of the companies in this field, but it could result in a very low number of injured people and damaged property. This in turn may facilitate a higher societal acceptance of UAM, as can be seen in the results of the survey. Indeed, the societal acceptance increases by 10 percent for the drone delivery use case and 9 percent for air taxi use case, if a safety bar similar to commercial aviation was implemented instead of one similar to the highest automotive one. Among the assessed cost dimensions were extensive redundancy requirements in the UAM system, longer duration of UAM introduction due to extended design and testing periods, as well as shorter intervals between maintenance checks of UAM aircraft and infrastructures. This was then qualitatively assessed to have a high impact on UAM system costs.
For the slightly lower safety bar, the associated increased risk to the population could be unacceptably high, and incidents or accidents could severely hamper the emerging UAM market. From a potential benefits point of view, this approach could offer a faster introduction of UAM services and therefore also provide faster assistance in battling ground-based traffic congestion. The cost impact was assessed as medium, due to a lower redundancy requirement, faster introduction of UAM services, and longer maintenance intervals for UAM aircraft and systems.
However safety is not a dimension where a business trade-off is acceptable in our society. Even a low number of accidents such as seen for autonomous cars can quickly cause a deterioration of public perception, thus the highest standards should be applied to UAM to foster its acceptance.
Possible security measures
In the security domain, possible measures on cyber security, security checks of passengers and counter UAM systems were assessed.
Cyber security should prevent the hacking of UAM communication and therefore avoid the malicious use or control of UAM. This could be done by encrypting the communication signals in the UAM system through hardware and software-based encryption. The cost impact could be medium and would potentially come from securing and monitoring the communications network and/or establishing a private command and control (C2) communication link.
954. Expectations and options for action
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Secondly, security checks of passengers similar to those carried out at airports could reduce the risk of malicious UAM use by the passengers. Equivalent checks could also offer the potential for passengers to directly connect to commercial aircraft at an airport gate. Measures could potentially include scanning of luggage and passengers, as well as limiting the carrying of liquids. The capital expenditure for the security technology, the increased personnel requirements at vertiports, and the potentially reduced competitiveness of UAM from the point of view of passenger time saving were some of the evaluated cost points which could result in a medium to high overall cost impact for UAM.
Finally, counter UAM systems, such as geofencing, might be needed to protect no-go areas from access by UAM. The acquisition and operation of counter UAM systems comes at a cost, but this was assessed to be low for the overall UAM system.
Possible measures on noise
The strength of focus on reducing the noise footprint of UAM will affect the overall cost impact on the UAM system.
The strong focus on reducing the noise footprint could risk significantly impacting the UAM business case due to high aircraft costs, but might bring the benefits of higher societal acceptance. This is indicated by the survey results, where a strong, rather than low, focus on reducing noise footprint increases public acceptance by 11 percent for the drone delivery and air taxi use cases. A strong focus on noise production could include measures like limiting aircraft noise to the level of a car at city speed and limiting the maximum aircraft on flight routes. Limiting the maximum number of aircraft on flight routes could also aid the societal acceptance of visual pollution. This would result in high aircraft costs, but could result in lower operating costs on defined routes due to a lower possible flight altitude and therefore shorter flight time for a set noise footprint on the ground. The overall cost impact of a strong focus on the UAM system could be medium to high.
A low focus on reducing noise footprint could severely reduce societal acceptance, but would make UAM more quickly available and could reduce the aircraft complexity. If the aircraft noise were limited to the level of a leaf blower or an old motorcycle, the aircraft costs could be lower. However, the operating cost might be higher, due to a higher flight altitude in order to achieve a set noise footprint on the ground. There could also be more noise-related complaints or law suits than for the strong focus on the noise footprint. This cumulates in a medium overall UAM system cost impact.
Possible measures on environment
From an environmental point of view two dimensions, wildlife protection and lifecycle sustainability, were each evaluated in a stronger and weaker form.
Strong environmental wildlife protection could help counteract a general reduction in biodiversity by increasing protection of local wildlife, but it could risk an economic impact on the UAM system. Potential measures in this domain could be the establishment of wildlife protection areas with no overflights, the implementation of bird avoidance systems, or specific onboard equipment like lights. This could lead to longer flight routes in order to avoid the wildlife protection areas, and the installation of bird avoidance systems either on the ground or in the UAM aircraft. For the overall UAM system, the implied costs of these potential measures were assessed as medium.
Weaker environmental wildlife protection could help local wildlife by simply establishing protection areas with limited or no overflights. The implied smaller restrictions to flight routes would result in an overall low-cost impact on the overall UAM system.
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A high lifecycle sustainability of UAM could reduce the risk of accelerating global warming and might bring create additional jobs in the value chain, while lowering the indirect cost of climate change. Possible measures could for example be a mandate for only using renewable power for the operations of UAM and requiring a recycling rate of more than 90 percent for the UAM aircraft and infrastructure. This could lead to higher costs for aircraft manufacturing and operations of the system, therefore the overall cost impact could be high.
A lower lifecycle sustainability of UAM could still somewhat reduce the risk of accelerating global warming, while maintaining a potentially low UAM system cost impact. A potential measure for this would be a mandate for some use of renewable power for operations and this could result in a lower cost impact for vehicle manufacturing and operations.
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5. Conclusions
99
This comprehensive study is the first of its kind and scope to measure the societal acceptance of UAM in the EU. New modes of transportation rarely appear, a recent example being autonomous cars, and their broad acceptance by society is typically linked to a series of factors. These include the maturity of certain technologies and how they are perceived in terms of threats or benefits by the public. The case of autonomous cars has demonstrated how difficult it is to predict exactly how a technical innovation will be adopted by users and by society in general.
For instance, as it was indeed difficult at this stage to provide real noise simulations or to quantify with certainty the number or frequency of UAM aircraft flying in a given city on a given day, it was also difficult to fully appreciate the annoyance this could cause.
It must therefore be kept in mind that this study has measured the attitude of the EU society towards the UAM at a given moment, i.e. early 2021, well in advance of future deployment in EU cities foreseen around 2024-2025. At this stage, information on UAM has been mostly reserved to specialised press and media and has not really reached the general public. Citizens therefore variously still perceive Urban Air Mobility as “science fiction” or “an exciting new concept”, but have not yet been exposed to actual operations and therefore lack tangible experience and feelings.
Globally, the study results tend to show that Urban Air Mobility concepts and operations benefit from a positive image and could be accepted by EU citizens, who are open to solutions improving the quality of life in the city and offering benefits for the common good. The acceptance would however be subject to respecting a number of guarantees and conditions to ensure that adequate levels of safety, security and environmental protection will be granted and that no citizen will suffer an undue and unbalanced nuisance from UAM.
It is now up to the UAM actors concerned, and in particular the regulatory authorities at all levels, to build on this initial positive premise and take measures to meet citizens’ expectations, so as to ensure that this initial open attitude translates into actual adoption by future users and acceptance from city residents.
For this reason, more specific studies, demonstrations and early implementation projects will likely be necessary as the concept further develops. Further information for the general public and guidance to national and local actors concerned will also be useful.
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Appendix100 A study on the societal acceptance of Urban Air Mobility in Europe
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Appendix 1: EASA’s UAM enabling activities
Aircraft airworthiness
certification of aircraft that are going to be flying in UAM environment on the basis of special Condition
a. For airworthiness certification for Manned VTOLs for operation in urban environment: SPECIAL CONDITION Vertical Take-Off and Landing (VTOL) Aircraft, Doc.Sc-VTOL-01, issue 1, 2 July 2019 Publication 2nd July 2019 - Special Condition for small-category VTOL aircraft;
b. For airworthiness certification of Light UAs medium risk that can also be operated in an urban environment: Special Condition Light Unmanned Aircraft Systems - Medium Risk, Doc. SC Light-UAS Medium Risk 01, Issue 1, 17 December 2020 Special Condition Light UAS Medium Risk ;
c. Guidelines on Design verification of UAS operated in the ‘specific’ category and classified in SAIL III and IV, Issue 1, 31 March 2021 Publication 8th April 2021 - Guidelines for the design verification of drones operated in the ‘specific’ category.
Operations
Launch of the regulatory developments aimed at introducing requirements for pilots/remote pilots/operators of these vehicles, operational requirements for operators, infrastructure such as vertiports, airspace integration aspects.
a. Open and Specific category: ED Decision 2020/022/R of 15 December 2020: Amendment 1 to the Acceptable Means of Compliance and Guidance Material to Commission Implementing Regulation (EU) 2019/947 and to the Annex (Part-UAS) thereto ‘AMC and GM to Commission Implementing Regulation (EU) 2019/947 — Issue 1, Amendment 1’ ‘AMC and GM to Part-UAS — Issue 1, Amendment 1 ED Decision 2020/022/R
b. Certified Category: Terms of reference for rulemaking task RTM.0230; Introduction of a regulatory framework for the operation of unmanned aircraft systems and for Urban Air Mobility in the European Union aviation system, Issue 2 — 04.06.2018; Introduction of a regulatory framework for the operation of unmanned aircraft systems and for Urban Air Mobility in the European Union aviation system, Issue 3 — DD.MM.YYYY ( not yet published)
Airspace integration
Adoption by the European Commission of the U-space regulatory package based on EASA technical Opinion (Opinion No 01/2020, High-level regulatory framework for the U-space, 13 March 2020 Publication on 13th March 2020 - Opinion 01/2020 ).
The U-space regulatory package is due to enter into force in autumn and applicable as from January 2023. The implementation of U-space will enable UAS operations in urban environment in safer and efficient manner and having due regard to other societal acceptance aspects such as environment and privacy and security.
Commission Implementing Regulation, amending Commission Implementing Regulation (EU) 2017/373 as regards requirements for providers of air traffic management/air navigation services and other air traffic management network functions in the U-space airspace designated in controlled airspace (publication expected in Autumn 2021)
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In addition, EASA has signed the Manifesto of the UAM initiatives by European cities under the UAM Initiative Cities Community (UIC2) of the EU’s Smart Cities Marketplace – formerly known as EIP-SCC Urban Air Mobility (UAM) Initiative 30th May 2018 - European Innovation Partnership in Smart Cities and Communities (EIP-SCC).
Finally, EASA is also engaged in a number of European research and demonstration projects, providing guidance and advice on the regulatory aspects.
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Appendix 2
In addition to chapter 2.2 (Target market identification), this chapter holds further information on identification of target markets. It also hols more detailed information on the surveys structure and on the methodology of quantitative survey question types (additions to chapter 3.1.1). Last but not east, the survey questions are attached in this Appendix.
Detailed information on target market identification
As explained in 2.2, for each of the six prioritised sub-use-cases defined in 2.1, the project identified the most attractive EU urban target markets for UAM OEMs and UAM operators from a business perspective. These target markets are likely to see initial deployment of commercial UAM services in the European Union and were therefore deemed to be important candidates for societal acceptance analysis.
The 4-step-methodology, explained in 2.2, led to an identification of 90 potential target markets (15 cities x 6 use cases) for initial OEM introduction.
Results of city selection
The following pages hold additional information to chapter 2.2 on use cases and metrics used to decide for the most relevant cities within the survey.
1. City viability for airport shuttle use case
Pre-selection for the airport shuttle use case was based on the following metrics: • Availability of an international airport. An airport is required for an airport shuttle service.• Population size. A large population of minimum 300,000 citizens is needed to ensure sufficient route utilisation.• Amount of non-transit passengers travelling between the airport and the city centre. An estimation of at
least 25,000 passengers are required for efficient utilisation of a route from an operator’s perspective.• GDP per capita level. Higher route utilisation rates are expected in cities with higher GDP per capita levels.
This led to a shortlist of 27 cities (see Figure 56).
103Appendix 2
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Figure 56: Target cities pre-selection process for the airport shuttle use case
Airport required for airport shuttle service
Large city population to ensure efficient utilization for route
Estima-tionrequires at least 25k passen-gers to efficient-ly utilize route for operator
389 172 75 36
Selection criteria
27
Higher utiliza-tionexpected with higher GDP per capita
yes
Availability of an airport
>300k
City population (Thousand inhabitants)
>25k
Number of non-transit passengers per day (Thousand)
Rat
iona
le#
cit
ies
Met
rics GDP per
capita (PPP in EUR)
>35k
Shortlist of cities
Austria Vienna
Belgium Brussels
Czech Republic
Prague
Denmark Copenhagen
Finland Helsinki
France Lyon, Paris, Toulouse
Hungary Budapest
Ireland Dublin
Italy Bologna, Milan, Rome
Nether-lands
Amsterdam
Poland Warsaw
Romania Bucharest
Spain Barcelona, Madrid
Sweden Stockholm
Germany Berlin, Bonn, Cologne, Dusseldorf, Frankfurt am Main, Hamburg, Munich, Stuttgart
These 27 cities were then ranked through a process based on the following KPIs: city size; expected number of trips; distance between the airport and city centre; travel time between the airport and the city centre with the fastest alternative travel type (e.g. taxi, car, or public transport) in rush hour; congestion rate; taxi cost for the journey to the airport; and suitable weather conditions (percentage of weather causes in total arrival delays, precipitation in mm per year). A weighting factor was assigned to each KPI to indicate how significant that KPI’s impact was on the overall ranking score (see Figure 57):
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Figure 57: Target cities ranking process for the airport shuttle use case
City
25%
WeightingKPI
Time saving
10%7 Suitable weather conditions (% of weather causes in total arrival delays, precipitation in mm per year)
5 Congestion rate
3 Distance between airport and city centre
4 Travel time between airport and city centre with fastest alternative travel type in the rush hour
25%City size1
25%Expected trip volumes2
6 Taxi expenses for ride from airport to city-centre
15%
Ranking (100 = best suitability for UAM)
Ranking of cities based on further KPIs
Paris 88.2
Milan 72.1
Berlin 78.3
Madrid 74.8
Rome 75.4
Vienna 70.4
Barcelona 71.4
Prague 72.8
Munich 75.3
Budapest 73.8
Dublin 70.8
Hamburg 62.8
Brussels 67.8
Amsterdam 65.6
Bucharest 67.4
Frankfurt am Main 59.9
Lyon 60.8
Stockholm 63.0
Warsaw 66.8
Stuttgart 63.9
Dusseldorf 56.8
Bonn 59.6
Toulouse 55.2
Copenhagen 56.2
Cologne 57.8
Bologna 59.8
Helsinki 58.2
Airport shuttle use case example
Remaining cities were ranked based on KPIs and top cities were pre-selected for next step
Country
The 15 top-ranking cities were further analysed in terms of the infrastructure available for UAM operations: availability of inner-city space for a heliport and availability of a river, motorway or corridor for noise avoidance; availability of mobility options for onward journeys (e-scooter, car-sharing etc.), as Figure 58 shows. All 15 cities passed the infrastructure assessment and entered consideration for final selection as a survey city.
105105Appendix 2
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Airport shuttle use case example
Pre-selected city
Ranking from previous steps (100 = best suited)
Availability of inner city space for heliport
Availability of river, highway or corridor for noise avoidance
Availability of mobility offer for onward journey (e-scooter, car-sharing, etc.)
Berlin 78.3
Madrid 74.8
Budapest 73.8
Barcelona 71.4
Dublin 70.8
Vienna 70.4
Brussels 67.8
Bucharest 67.4
Warsaw 66.8
Amsterdam 65.6
PRE-READ
Information for timeline assessment
Paris 88.2 Volocopter pilot in 2021
Rome 75.4
Munich 75.3 Not feasible until 2025 as distance to airport too long
Prague 72.8
Milan 72.1 Not feasible until 2025 as distance to airport too long
As final step a cities infrastructure assessment for UAM operations and check of implementation timelines was conducted
Figure 58: Cities infrastructure assessment for the airport shuttle use case
2. City viability for sightseeing use case
Pre-selection for the sightseeing use case was based on the following metrics: • Number of international visitors per year. A leading position in terms of number of visitors per year is required as
a signal for sufficient demand for potential UAM operators.• EU membership. Only EU cities were in scope.
From 389 EU cities, 100 were identified as having a large amount of international visitors per year (over 2.4 million), as Figure 59 shows.
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Figure 59: Target cities pre-selection process for the sightseeing use case
Austria Vienna
Belgium Brussels
Czech Republic
Prague
Denmark Copenhagen
France Nice, Paris
Hungary Budapest
Ireland Dublin
Italy Florence, Milan, Rome, Venice
Nether-lands
Amsterdam
Poland Cracow, Warsaw
Spain Barcelona, Madrid
Sweden Stockholm
Germany Berlin, Frankfurt am Main, Munich
Greece Athens, Heraklion, Rhodes
Portugal Lisbon, Porto
389 100 32
Selection criteria
26
Leading position (top 100 worldwide) in number of visitors per year is required as signal for sufficient demand towards potential operators
Standard preselection of Euromonitor
In scope for EASA and European Commission
>2.4 Mn Yes
International visitors per year (Mn)
European country
Member of European Union
Yes
Shortlist of cities
Rat
iona
le#
cit
ies
Met
rics
The 26 pre-selected cities were then ranked in accordance with the following KPIs: addressable market (number of international visitors per year, expenditure on entertainment and sightseeing per visitor and per trip); number of similar tourism offerings in terms of scope (e.g. architectural tour, towers and viewing platforms, helicopter trips) and budget (price over EUR 100) as found on getyourguide.com; suitable weather conditions, measured on the basis of ATFM delays due to bad weather; and attractiveness (e.g. architecture) of city for aerial sightseeing according to expert opinion. A weighting factor was assigned to each KPI to indicate how significant that KPI’s impact was on the overall ranking score (see Figure 60):
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Figure 60: Target cities ranking process for the sightseeing use case
1. Similar offerings regarding scope (e.g., architectural tour, towers & viewing platforms, helicopter trips) and budget (price >100 EUR) as found on getyourguide.com
Ranking of cities based on further KPIs
WeightingKPI
25%3 Number of similar touristic offerings1
5 Attractiveness (e.g., architecture) of city for aerial sightseeing according to expert opinion
25%
City
1
2
Number of international visitors per year
Expenditure for entertainment & sightseeing per visitor and per trip
25%Addressable market
4 Suitable weather conditions, measured as annual precipitation
25%
Paris 93.75
Budapest 75.00
Rome 87.50
Prague 81.25
Amsterdam 81.25
Stockholm 68.75
Berlin 68.75
Florence 75.00
Venice 81.25
Barcelona 81.25
Porto 56.25
Madrid 68.75
Lisbon 62.50
Athens 68.75
Munich 43.75
Cracow 50.00
Warsaw 56.25
Nice 62.50
Vienna 56.25
Frankfurt am Main
68.75
Heraklion 37.50
Rhodes 43.75
Dublin 37.50
Milan 43.75
Brussels 37.50
Country
Ranking (100 = best suitability for UAM)
Copenhagen 31.25
The top-ranking 15 cities were finally analysed in terms of the infrastructure available for UAM operations: availability of space near tourist hotspots (see Figure 61). All 15 cities passed the infrastructure assessment and were selected for the next step.
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Figure 61: City infrastructure assessment for the sightseeing use case
Ranking from previous steps (100 = best suited)City
Availability of space near tourist hotspots
93.75Paris
75.00Budapest
87.50Rome
81.25Prague
81.25Amsterdam
68.75Stockholm
68.75Berlin
75.00Florence
81.25Venice
81.25Barcelona
68.75Madrid
62.50Lisbon
68.75Athens
62.50Nice
68.75Frankfurt am Main
Country
3. City viability for first aid use case
Pre-selection for the first aid use case was based on the following metrics: • Availability of an emergency rendezvous system (emergency doctor arriving at an accident location
before and separately from an ambulance vehicle). This sub-use-case is most likely to be firstly implemented in countries with the rendezvous emergency system.
• City population. A high number of inhabitants (more than 300,000 citizens) is required as a signal for sufficient demand for potential operators.
• Accident statistics. A high number of accidents per city inhabitant is a signal for sufficient demand for a first aid service.
• Congestion rate. A high congestion rate indicates the added value of UAM first aid service.
From a long list of 389 EU cities, 316 were identified as being in countries that have a rendezvous emergency system, and 66 of these were further selected as having a large population (over 300,000 citizens). In the next step 64 of the 66 were identified as having a high number of accidents per city inhabitant (more than 1 accident/1,000 inhabitants). In the last step 19 of these 64 cities, which have a high congestion rate (over 30 percent), were pre-selected for the ranking process (see Figure 62).
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Figure 62: Target cities pre-selection process for the first aid use case
The use case will be implement-ted firstly in countries with the rendezvous emergency system (emergency doctor arriving separately)
High amount of inhabitants is required as a signal for sufficient demand towards potential operators
High amount of accidents per city inhabit-ant is a signal for sufficient demand for a first aid service
389 316 66 64
Selection criteria
19
High conges-tion rate indicates a necess-ity for a UAM first aid service
yes
Availability of an airport
>300k
City population (Thousand inhabitants)
>25k
Number of non-transit passengers per day (Thousand)
GDP per capita (PPP in EUR)
>35k
Shortlist of cities
Belgium Brussels
Czech Republic
Prague, Brno
France Lyon, Marseille, Nice, Paris
Germany Berlin, Hamburg, Munch, Nurnberg, Stuttgart
Italy Genoa, Milan, Naples, Palermo, ROME
Hungary Budapest
Ireland Dublin
Rat
iona
le#
cit
ies
Met
rics
The 19 pre-selected cities were ranked in accordance with the following KPIs: number of accidents per 1,000 inhabitants and number of hospitals per 100,000 inhabitants. A weighting factor was assigned to each KPI to indicate how significant that KPI’s impact was on the overall ranking score (see Figure 63). Cities with the highest number of accidents and lowest number of hospitals obtained the highest ranking score.
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Figure 63: Target cities ranking process for the first aid use case
City
Ranking of cities based on further KPIs
WeightingKPI
Number of accidents per 1K inhabitants
1
50%Number of hospitals per 100K inhabitants
2
Ranking (100 = best suitability for UAM)Country
83,3Paris
45,8Milan
75Berlin
62,5Budapest
45,8Dublin
75Hamburg
66,7Lyon
58,3Stuttgart
66,7Nice
66,7Marseille
62,5Genova
45,8Rome
45,8Napoli
45,8Palermo
45,8München
58,3Nurnberg
45,8Brussels
62,5Prague
62,5Brno
50%
The 15 top-ranking cities were analysed in terms of the infrastructure available for UAM operations: availability of inner-city space for a heliport and availability of a river, motorway or corridor for noise avoidance (see Figure 64). All 15 cities passed the infrastructure assessment and were selected for the next step.
111111Appendix 2
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Figure 64: Cities infrastructure assessment for the first aid use case
Metropolitan area
Ranking from previous steps (100 = best suited)
Availability of innercity space for heliport
Availability of river, highway or corridor for noise avoidance
Hamburg 75
Paris 83,3
Berlin 75
Lyon 66,7
Marseille 66,7
Nice 66,7
Genova 62,5
Prague 62,5
Brno 62,5
Nurnberg 58,3
Stuttgart 58,3
Budapest 62,5
Milano 45,8
Roma 45,8
45,8Dublin
45,8München
4. City viability for Last-mile delivery
Pre-selection for the last-mile delivery use case was based on the following metrics: • City population. A high number of potential customers is required as a signal for sufficient demand for potential
operators.• Percentage of online ordering. A high number of inhabitants ordering online is required as a signal for sufficient
demand for potential operators.• Population density. Low population density is required to enable a ‘garden delivery’ option and build delivery
stations for a ‘pre-defined route’ option.• Availability of same day delivery. Availability of same day delivery is an indicator for sufficient demand for
delivery services in the city.
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Figure 65: Target cities pre-selection process for the last-mile use case
Prague, BrnoCzech Republic
AarhusDenmark
RotterdamNether-lands
Saragossa, Murcia, Palma de Mallorca
Spain
GothenburgSweden
Hamburg, Nurnberg, Bonn, Bremen, Bochum, Düsseldorf, Dresden, Dortmund, Duisburg, Cologne, Essen, Hannover, Leipzig, Mannheim, Münster, Wuppertal
Germany
HelsinkiFinland
TallinnEstonia
Slovakia Bratislava
1. Average value for EU
High amount of potential customers is required as a signal for sufficient demand towards potential operators
High amount of inhabitants ordering online is required as a signal for sufficient demand towards potential operators
Low popular-tion density is required to enable a “garden delivery” option and build delivery stations for “pre-defined route” option
389 236 56 31
Selection criteria
26
Availability of same day delivery is an indicator for sufficient demand for delivery services
>300
Availability of an airport
>631
City population (Thousand inhabitants)
<3
Number of non-transit passengers per day (Thousand)
GDP per capita (PPP in EUR)
Yes
Shortlist of cities
Rat
iona
le#
cit
ies
Met
rics
From a long list of 389 EU cities, 236 were identified as having a large population (over 300,000 citizens). 56 of these were further selected as having a high percentage of online ordering (over 63 percent, which is the average percentage of online ordering in the EU). In the next step, 31 of these 56 cities were identified as having low population density (less than 3,000 inhabitants/km2). In the last step, 26 of these 31 cities that have a same day delivery option were pre-selected (see Figure 65).
7.4.2 The 26 pre-selected cities were then ranked in accordance with the following KPIs: number of food delivery app downloads per inhabitant, availability of Amazon delivery centres in the city and average congestion rate per city. A weighting factor was assigned to each KPI to indicate how significant that KPI’s impact was on the overall ranking score (see Figure 66).
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KPI Weighting
3 Average congestion per city
30%
1 Number of food delivery app downloads per inhabitant
35%
35%Availability of amazon delivery centres in the city
2
Ranking of cities based on further KPIs
Country
50Murcia
73,3Rotterdam
61,7Cologne
73,3Hamburg
69,2Tallinn
61,7Dusseldorf
61,7Dortmund
65,8Hanover
69,2Helsinki
69,2Bonn
61,7Essen
50Prague
61,7Leipzig
57,5Bratislava
61,7Dresden
City
Ranking (100 = best suitability for UAM)City Country
50Aarhus
50Nuremberg
50Bremen
42,5Saragossa
42,5Bochum
50Münster (West-phalia)
50Duisburg
50Mannheim
38,3Palma de Mallorca
Ranking (100 = best suitability for UAM)
30,8Gothen-burg
42,5Wuppertal
45,8Brno
Figure 66: Target cities ranking process for the last-mile delivery use case
7.4.3 The top-ranking 15 cities were analysed in terms of the infrastructure available for UAM operations: availability of inner-city space for a heliport and availability of a river, motorway or corridor for noise avoidance (see Figure 67). All 15 cities passed the infrastructure assessment and were selected for the next step.
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Metropolitan area
Ranking from previous steps (100 = best suited)
Availability of innercity space for heliport
Availability of river, highway or corridor for noise avoidance
Helsinki 69,2
Rotterdam 73,3
Hamburg 73,3
Tallinn 69,2
Bonn 69,2
Hanover 65,8
Cologne 61,7
Dortmund 61,7
Dresden 61,7
Essen 61,7
Leipzig 61,7
Dusseldorf 61,7
Bratislava 57,5
Prague 50
50Murcia
Figure 67: Cities infrastructure assessment for the last-mile delivery use case
5. City viability for Medical supply delivery
Pre-selection for the medical supply delivery use case was based on the following metrics:
• City population. A high number of inhabitants is required as a signal for sufficient demand for potential operators.• Congestion rate. A high congestion rate indicates the need for UAM medical supply services.
From a long list of 389 EU cities, 89 were identified as having a large population (over 300,000 citizens); from these 32 were further selected as cities with a high congestion rate (over 30 percent), as Figure 68 shows.
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Czech Republic
Prague, Brno
Germany Berlin, Hamburg, Munich, Nurnberg, Stuttgart
Belgium Brussels
France Lyon, Marseille, Nice, Paris
Italy Genoa, Milan, Naples, Palermo, Rome
Hungary Budapest
Bulgaria Sofia
Estonia Tallinn
Greece Athens, Thessaloniki
Ireland Dublin
Poland Cracow, Poznan, Szczecin, Warsaw, Wroslaw, Gdansk
Portugal Lisbon
Romania Bucharest
Slovakia Bratislava
Lithuania Vilnius
Croatia Zagreb
Selection criteria
>30%
High amount of inhabitants is required as a signal for sufficient demand towards potential operators
High congestion rate indicates a necessity for UAM medical supply services
389 89 32
Cities population(Thousand)
Congestion rate(%)
>300
Shortlist of cities
Rat
iona
le#
cit
ies
Met
rics
Figure 68: Target cities pre-selection process for the medical supply delivery use case
The ranking process for the 32 pre-selected cities was based on the following KPIs: number of hospitals per 1,000 inhabitants and average congestion rate. A weighting factor was assigned to each KPI to indicate how significant that KPI’s impact was on the overall ranking score (see Figure 69). Cities with the lowest density of hospitals and highest congestion rate obtained the highest ranking.
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Ranking of cities based on further KPIs
WeightingKPI
50%Low number of hospitals per 1K inhabitants
1
50%High average congestion rate2
Ranking (100 = best suitability for UAM)City
Country
100Cracow
90Wroclaw
65Hamburg
75Budapest
85Poznan
85Bucharest
75Sofia
85Warsaw
65Berlin
65Gdansk
70Dublin
70Prague
65Vilnius
75Bratislava
60Paris
Figure 69: Target cities ranking process for the medical supply delivery use case
The 15 top-ranking cities were analysed in terms of the infrastructure available for UAM operations: availability of inner-city space for a heliport and availability of a river, motorway or corridor for noise avoidance (see Figure 70). All 15 cities passed the infrastructure assessment and were selected for the next step.
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Metropolitan area
Ranking from previous steps (100 = best suited)
Availability of innercity space for heliport
Availability of river, highway or corridor for noise avoidance
Hamburg 75
Paris 83,3
Berlin 75
Lyon 66,7
Marseille 66,7
Nice 66,7
Genova 62,5
Prague 62,5
Budapest 62,5
Stuttgart 58,3
Milano 45,8
Nurnberg 58,3
Roma 45,8
Dublin 45,8
45,8München
Figure 70: Cities infrastructure assessment for the medical supply delivery use case
6. City viability for fixed metropolitan/regional network
Pre-selection for the metropolitan use case was based on the following metrics:
• Number of medium- and high-income households. Higher utilisation is expected in regions with a higher number of medium- and high-income households.
• Metropolitan size. The metropolitan/regional network would connect large and medium-size cities of a same region, within a single Member State or cross border; A large metropolitan/regional population is required to enable efficient route utilisation (i.e. minimum 2 million citizens).
• Availability of at least two medium-size cities within a metropolitan area. At least two medium-size cites are required to ensure sufficient route utilisation.
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Austria Vienna region
Belgium Belgian central metro region
Denmark Oresun region (Copenhagen)
France Paris region
Italy Rome and Milan regions
Spain Barcelona region
Germany Rhein-Ruhr, Rhein-Main, Rhein-Neckar, Stuttgart and Munich regions
Nether-lands
Noord-Braband(Eindhoven, Tilburg, Breda)
Poland Warsaw region
91 52 20
Selection criteria
16
Higher utilization expected in regions with higher number of households with medium and high income
Large metropolitan population required to enable efficient utilization for route
Estimation requires at least two medium size cites to efficiently utilize route for operator
>40% >2 Mn inhabitants
Ratio-nale
# cities
Metric Metropolitan regions with high number of households with medium and high income (%)
Metropolitan size
(Mn inhabitants)
At least two medium size cities within a metropolitan region
Yes
Shortlist of cities
Figure 71: Target cities pre-selection process for the fixed metropolitan network use case
From 91 EU metropolitan areas, 52 were identified as having a high number of medium (over EUR 25,000) and high (over EUR 60,000) annual income households, and 20 of these were further selected as regions with a large population (over 2 million inhabitants). In the last step, 16 of these 20 metropolitan areas had at least two medium-size cities and were pre-selected (see Figure 71).
7.6.2 The 16 pre-selected metropolitan areas were ranked in accordance with the following KPIs: metropolitan size; distance between cities; time saved when travelling with UAM instead of the fastest alternative method (train, taxi, airport shuttle bus); GDP per capita; concentration of destination points within a metropolitan area; and suitable weather conditions (percentage of weather causes in total arrival delays, precipitation in mm per year). A weighting factor was assigned to each KPI to indicate how significant that KPI’s impact was on the overall ranking score (see Figure 72).
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WeightingKPI
5%6 Suitable weather conditions (% of weather causes in total arrival delays, precipitation in mm per year)
25%3 Time saving when traveling with UAM instead of fastest alternative source (train, taxi, airport shuttle bus)
25%Metropolitan size1
25%Distance between the cities2
4 GDP per capita 10%
5 Concentration of destination points with one metropolitan region
10%
Ranking of cities based on further KPIs
Metropolitan area Example route
75.8Milan region Milan-Monza
78.8Rome region Rome-Ostia
72.5Stuttgart region Stuttgart-Reutlingen
73.8Rhein-Neckar Mannheim-Heidelberg
68.3Oresund Copenhagen-Hillerod
62.9Munich region Munich-Heidelberg
79.6Belgian central metro
Brussels-Leuven
79.2Rhein-Ruhr Cologne-Bonn
73.8Barcelona region
Barcelona-Mataro
58.8Paris region Paris-Chantilly
Ranking (100 = best suitability for UAM)
58.8Rhein-Main Frankfurt-Aschaffenburg
59.2Vienna regions Vienna-Baden
Figure 72: Target cities ranking process for the metropolitan network use case
The 15 top-ranking cities were analysed in terms of the infrastructure available for UAM operations: availability of inner-city space for a heliport and availability of a river, motorway or corridor for noise avoidance; availability of mobility options for onward journeys (e-scooter, car-sharing etc.) (see Figure 73). All 15 metropolitan areas passed the infrastructure assessment and were selected for the next step.
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Austria Vienna region
Belgium Belgian central metro region
Denmark Oresun region (Copenhagen)
France Paris region
Italy Rome and Milan regions
Spain Barcelona region
Germany Rhein-Ruhr, Rhein-Main, Rhein-Neckar, Stuttgart and Munich regions
Nether-lands
Noord-Braband(Eindhoven, Tilburg, Breda)
Poland Warsaw region
91 52 20
Selection criteria
16
Higher utilization expected in regions with higher number of households with medium and high income
Large metropolitan population required to enable efficient utilization for route
Estimation requires at least two medium size cites to efficiently utilize route for operator
>40% >2 Mn inhabitants
Ratio-nale
# cities
Metric Metropolitan regions with high number of households with medium and high income (%)
Metropolitan size
(Mn inhabitants)
At least two medium size cities within a metropolitan region
Yes
Shortlist of cities
Figure 73: Cities infrastructure assessment for the fixed metropolitan network use case
Selection of the six cities or regions for the survey
The top 15 cities or regions across the discussed city selection use cases and the guiding principles were the basis for the selection of the six cities or regions, where the survey was to be conducted. The final selection of the survey cities was made during a meeting with the Executive Director of EASA and contained the cities of Barcelona, Budapest, Hamburg, Milan, and Paris, as well as the Øresund region between Denmark and Sweden.
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Detailed information on questionnaire structure
The questionnaire was designed to assess, understand and measure the most important societal acceptance drivers for UAM, including perceived benefits and concerns and what it takes to increase societal acceptance. The questionnaire included 36 questions; the response time being estimated at twenty-five minutes.
Complementing the general attitude towards various use cases and the relative importance of them, two overarching use cases that are easy to imagine and self-explanatory for non-experts were identified in the quantitative survey to determine the levels of acceptance: the delivery of goods by drone and the transport of passengers by air taxi. An in-depth analysis was conducted to measure the relative acceptance levels of three key concerns, identified through the literature review: of safety, noise and visual annoyance in an urban environment, possibly tinted by cultural differences. Finally, the questionnaire addressed the general attitude and expectations of respondents towards regulatory authorities. For an English version of the questionnaire that was distributed online to the participants, please refer to the Appendix.
Part 1: Screener
Part 1 of the survey ensured that participants meet the predefined criteria (see the predefined quotas above). An informational video of 1 minute and 36 seconds was presented to ensure the participants’ prior information as well as general and common understanding of UAM. The use cases shown in the video were passenger transport by air taxi, express delivery of food by drone, transport of emergency medical services to the scene of an accident, and delivery of medical supplies to a hospital. The selection aimed for a balanced representation of commercial and public service use cases, drone and passenger use cases, as well as use cases with a pilot on-board and remotely piloted. The vehicles depicted in the video were invented and did not correspond to any industrial product existing or in development. The objective was to give a general feeling and idea, rather than to reflect actual technical accuracy. The video concluded with the message that Urban Air Mobility is coming soon to Europe. The video did not include any sound other than music, as noise perception was evaluated in a separate survey.
Subsequently, this section checked whether participants fell into one of the pre-selected potential user groups, i.e. delivery of goods by drone and/or transport of passengers by air taxi.
Part 2: General attitude towards UAM
In a first step, participants’ affinity to make use of new technologies was assessed. This was intended to help understand their attitude towards innovation. Participants were then asked to rank the usefulness of fourteen UAM use cases. In this way, the use cases considered the most important for the population could be identified. Participants were also given an overview of several conceivable use cases that go beyond those shown in the video.
In a second stage, participants were asked to evaluate the most important overarching perceived benefits and opportunities (not use-case specific) that UAM could entail. In addition, an open question invited them to name further positive aspects that did not appear in the survey and may not have been covered by the literature so far.
Part 3: Delivery by drone
To ensure participants did not mix responses for different types of vehicles (drones and air taxis), questions on drones were asked first, before questions on air taxis were asked separately in Part 4.
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In a first step, participants’ propensity to make use of current express delivery options was assessed. This was intended to help classify their views on the potential usefulness of express delivery options by drones. In a second step, they were asked to rank the most important perceived benefits of goods delivery by drones.
The core element of this section was the investigation into the public’s concerns for this type of use-case. In general, questions were phrased in such a way to understand the relative importance of concerns among the population – whereas an absolute question such as ‘How important is security to you?’ would have led to a potential selection providing little to no insight (‘Everything is important’). The concerns were measured through six groups of questions.
• The first question addressed participants’ basic comfort level with the idea of unmanned air taxis. Air taxis pose a potential safety threat not only to the passengers using them, but also to pedestrians on the ground. As pedestrians will be affected by air taxis despite not necessarily choosing to use air taxis themselves, the hypothesis was that the perceived safety of pedestrians will have a significant impact on the societal acceptance of air taxi operations.
• In a second question, participants were asked to rank the most important concerns, in their view, regarding the operation of air taxis in an urban environment. This list of concerns was derived from the literature review.
• The third question was a deep dive into safety, noise and visual annoyance concerns using a conjoint analysis. The question aimed to determine which levels of these issues came within an acceptable range for a broad population.
• In the fourth question, environmental concerns were compared to determine their relative importance among the population.
• A sixth group of questions addressed affordability, privacy, job losses, allocation of inner-city space to take-off and landing stations, and security threats for local residents. These were intended to elicit the participants’ expectations and fears, both rational and irrational.
• Lastly, participants were asked to rank the most important concerns, in their view, related to take-off and landing stations (‘vertiports’).
Part 4: Passenger transport (air taxi)
Part 4 was structured analogously to Part 3 and the same methodology and reasoning applied for most questions. Only in some points adjustments were made to fit the use case. In a first step, participants’ travel habits were evaluated to assess their general propensity to fly.
• The first question was on how often they travel by aeroplane for personal or business reasons in a typical year.• The second question asked them how they typically get to and from the airport. This was intended to help classify
their openness to try out air taxis, specifically to airports. • Next, they were asked to rank the most important perceived benefits of passenger transport by air taxi. • The next set of questions were designed to measure concerns. First, participants were asked to rank different
statements that people might make about air taxis. Four statements for ranking were given to identify the participants’ basic comfort level with the idea of unmanned air taxis – for both passengers and pedestrians.
Air taxis pose a potential safety threat not only to the passengers using them, but also to pedestrians on the ground. As pedestrians will be affected by air taxis despite not necessarily choosing to use air taxis themselves, the hypothesis was that the perceived safety of pedestrians will have a significant impact on the societal acceptance of air taxi operations.
• With the fifth question, participants were asked to choose their most important concerns.• After that, in the sixth question they were ask to rank the most important concerns, in their view, regarding the
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operation of air taxis in an urban environment. This list of concerns was derived from the literature review. • Participants were then asked to imagine themselves in three different situations in 2030, involving air taxis.
Participants were then asked to choose their preferred option out of the alternatives.• This was followed by rating given scenarios in terms of their acceptance.
After this, there was a deep dive into safety, noise and visual annoyance concerns using a conjoint analysis. The question aimed to determine which levels of these issues were deemed within an acceptable range for a broad population.
• Next, environmental concerns were compared to determine their relative importance among the population.• Separate questions on affordability, privacy, job losses, allocation of inner-city space to take-off and landing
stations, and security threats for local residents were designed to elicit the participants’ expectations and fears, both rational and irrational.
• Another question on vertiports helped to understand how pleased participants would feel to have a take-off station close by.
• Lastly, participants are asked to rank their most important concerns related to take-off and landing stations.
Part 5: Regulators and their role
As the study was commissioned by EASA, for future regulatory work, participants’ attitudes towards European, national, regional or local authorities deserved special focus.
• Therefore, the survey polled participants’ trust levels in the aforementioned authorities to adopt regulations and to handle the risks associated with UAM.
Part 6: Further understanding of security and environmental aspects
In order to find out more on environmental and cybersecurity concerns, the survey also polled participants’ trust levels in the security and cyber security of drones and air taxis.
• First, participants were asked to what extent they trust that advanced aircraft flying in an urban environment will be technologically secure and armed against threats from hackers.
• Secondly, participants were asked whether and to what extent their trust level would increase with measures and regulations to manage cyber security risks. They were asked to compare authorities.
• Furthermore, the desirability of an ecolabel for UAM was assessed. This question was visually illustrated by a sample ecolabel.
Part 7: Additional demographic questions
Before the end of the survey, a few additional demographic questions were asked, such as the number of people living in the participant’s household, their level of education, total gross household income and current occupation in order to be able to assess potential differences in the respondents’ groups.
Methodological basis of survey question types
Different types of questions are suitable for different objectives and the choice of question type should be guided by the aim of the question. Multiple-selection, rating as well as forced-ranking questions were used. In the following, the rationales behind each are described briefly.
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Multiple-select questions
Multiple-select questions allow survey participants to choose one or more answers from a set of provided options. The order of the options may be randomised to avoid bias, as survey respondents have a tendency to choose options positioned higher on the list. The advantage of this question type is that it gives participants the freedom to select several options and not feel restricted in their answers. Question A4 (see next Appendix section) is an example of this question type, when participants are asked to pick their top three personal benefits related to UAM.
For example, it is suitable for enquiring about participants’ motivations for an attitude or decision. When evaluating the survey responses, the relative frequency of mentions of each option can be compared and their importance assessed.
A slight restriction can be imposed by introducing an upper limit on the number of choices that participants may select. This forces participants to prioritise. The number of mentions of an answer among the top three, for example, can thus be evaluated.
A special case is single-punch questions, where only one answer can be selected. These are suitable for asking about extremes (e.g. ‘What is your top priority?’) or when options are mutually exclusive (yes or no).
Rating questions
Rating questions allow participants to weigh answers or statements via grades or numerical scales. They are often used when participants are asked about their level of approval or acceptance.
The grades can be transferred into a numerical evaluation via a Likert scale, which allows, for example, the comparison of approval rates for different statements or population batches such as age or gender groups.
An example of this question type is Question B6 (see next Appendix section). Here, the level of comfort with different drop-off locations for drone parcel delivery is queried to ultimately understand which option leads to the highest possible approval rate across the population.
Forced-ranking questions
Forced ranking questions enable participants to compare items and to rank them according to their preference. Again, the items may be randomised to avoid bias for higher-positioned answers.
The advantage of this question type is that participants consciously sort the items and indicate each item’s relative personal importance; whereas in multiple-select questions, items are either important or unimportant, and in rating questions, items can be assigned the same absolute grade. Therefore, this type of question helps make relative differences visible, providing critical insights into participants’ individual preferences and the preferences of the panel overall. In this way, within a set of important issues, the most important ones can be identified. An example of this question type is Question B9 (see next Appendix section), where the aim is to get to know the most critical environmental concerns for respondents among an overall set of key environmental concerns.
A special approach was taken in Questions B4 to B5 (see next Appendix section). Since forced-ranking questions are only suitable for a limited number of items (six to seven items is considered the upper bound, as the process becomes confusing beyond that), a combination of multiple-select questions followed by forced-ranking questions was chosen In this way, participants had the opportunity to pre-select the concerns that they considered most important, before
125125Appendix 2
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being asked to give a relative assessment for this subset. This way, the important concerns are separated from the unimportant ones, and the especially important ones are then ranked.
A similar approach was taken in Questions A2 to A3 (see next Appendix section). First, participants are asked to select the most useful use cases, in their view, in each of the following categories: delivery, medical emergency transport and passenger transport. In order to make the usefulness comparable across the categories, the top two from each category are transferred to Question A3 and are then subject to forced ranking. In this way, the most useful use cases are identified, and conclusions can be drawn about the categories that are perceived as fundamentally useful.
Detailed information on methodology of quantitative survey question types and questionnaire
Different types of questions are suitable for different objectives and the choice of question type should be guided by the aim of the question. Multiple-selection, rating as well as forced-ranking questions were used. For more details on the question types used, see Appendix.
Multiple-select questions
Multiple-select questions allow survey participants to choose one or more answers from a set of provided options. The order of the options may be randomised to avoid bias, as survey respondents have a tendency to choose options positioned higher on the list. The advantage of this question type is that it gives participants the freedom to select several options and not feel restricted in their answers. Question A4 (see the Appendix) is an example of this question type; participants must pick their top three personal benefits related to UAM.
For example, it is suitable for enquiring about participants’ motivations for an attitude or decision. When evaluating the survey responses, the relative frequency of mentions of each option can be compared and their importance assessed.
A slight restriction can be imposed by introducing an upper limit on the number of choices that participants may select. This forces participants to prioritise. The number of mentions of an answer among the top three, for example, can thus be evaluated.
A special case is single-punch questions, where only one answer can be selected. These are suitable for asking about extremes (e.g. ‘What is your top priority?’) or when options are mutually exclusive (yes or no).
Rating questions
Rating questions allow participants to weigh answers or statements via grades or numerical scales. They are often used when participants are asked about their level of approval or acceptance.
The grades can be transferred into a numerical evaluation via a Likert scale, which allows, for example, the comparison of approval rates for different statements or population batches such as age or gender groups.
An example of this question type is Question B6 (see Appendix). Here, the level of comfort with different drop-off locations for drone parcel delivery is queried to ultimately understand which option leads to the highest possible approval rate across the population.
126 A study on the societal acceptance of Urban Air Mobility in Europe126
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Forced-ranking questions
Forced ranking questions enable participants to compare items and to rank them according to their preference. Again, the items may be randomised to avoid bias for higher-positioned answers.
The advantage of this question type is that participants consciously sort the items and indicate each item’s relative personal importance; whereas in multiple-select questions, items are either important or unimportant, and in rating questions, items can be assigned the same absolute grade. Therefore, this type of question helps make relative differences visible, providing critical insights into participants’ individual preferences and the preferences of the panel overall. In this way, within a set of important issues, the most important ones can be identified. An example of this question type is Question B9 (see Appendix), where the aim is to get to know the most critical environmental concerns for respondents among an overall set of key environmental concerns.
A special approach was taken in Questions B4 to B5 (see Appendix). Since forced-ranking questions are only suitable for a limited number of items (six to seven items is considered the upper bound, as the process becomes confusing beyond that), a combination of multiple-select questions followed by forced-ranking questions was chosen In this way, participants had the opportunity to pre-select the concerns that they considered most important, before being asked to give a relative assessment for this subset. This way, the important concerns are separated from the unimportant ones, and the especially important ones are then identified.
A similar approach was taken in Questions A2 to A3 (see Appendix). First, participants are asked to select the most useful use cases, in their view, in each of the following categories: delivery, medical emergency transport and passenger transport. In order to make the usefulness comparable across the categories, the top two from each category are transferred to Question A3 and are then subject to forced ranking. In this way, the most useful use cases are identified, and conclusions can be drawn about the categories that are perceived as fundamentally useful.
127Appendix 2
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A1.
Aff
init
y to
new
tech
nolo
gies
1. C
ar re
ntal
, e.g
. Six
t, E
urop
car,
Avi
s
2.
Ride
hai
ling,
i.e.
ord
erin
g a
car s
imila
r to
a ca
b vi
a a
mob
ile a
pp, s
uch
as F
ree
Now
, Ube
r, B
olt,
it T
axi,
Ube
r, C
abify
, Moo
ve, V
iggo
1
Nev
er u
sed
and
not i
nter
este
d
Hav
e no
t use
d ye
t, b
ut w
ould
co
nsid
er it
A fe
w ti
mes
a
year
6 to
10
tim
es a
ye
arEv
ery
2 w
eeks
Onc
e a
mon
thEv
ery
wee
k or
m
ore
ofte
nTe
chno
logy
or s
ervi
ce
1.O
nly
rele
vant
pro
vide
rs in
resp
ectiv
e ci
ties
are
show
n
6. O
nlin
e bo
okin
g or
boo
king
via
app
of m
obili
ty ti
cket
s, e
.g. f
or tr
ains
, fli
ghts
, bus
es, o
ther
pub
lic tr
ansp
ort
8. D
rone
s, e
.g. f
or p
hoto
s, v
ideo
s or
recr
eatio
n
9. F
lat f
ee o
r sub
scrip
tion
mod
el fo
r the
del
iver
y of
onl
ine
orde
rs
(of g
roce
ries,
con
sum
er e
lect
roni
cs, a
ppar
el, e
tc.),
suc
h as
Am
azon
Pr
ime,
OTT
O U
P, Z
alan
doPl
us, C
disc
ount
à vo
lont
é, F
nac+
, Pc
Com
pone
ntes
Prem
ium
1
3. C
ar s
harin
g, e
.g. S
hare
Now
, Mile
s, C
ambi
o, g
etar
ound
, Ube
eqo,
en
joy,
lets
go, G
reen
Mob
ility
, Gre
enG
o, M
OL
Lim
o1
4. R
ide
shar
ing
(i.e.
sha
ring
a ta
xi o
r sim
ilar w
ith o
ther
peo
ple
taki
ng a
si
mila
r rou
te),
e.g.
Ube
rPoo
l, M
oia1
5. (
E-)b
ikes
, (e-
)sco
oter
s or
ele
ctric
kic
k sc
oote
rs fr
om s
harin
g pr
ovid
ers,
suc
h as
Lim
e, T
ier,
em
my,
City
scoo
t, D
ott,
GoV
olt,
Bird
, V
aim
oo, M
OL
bubi
, ogr
e&co
1
7. V
ideo
con
fere
ncin
g, e
.g. Z
oom
, Mic
roso
ft T
eam
s, S
kype
A1.
Fre
quen
cy o
f usa
ge o
f new
tech
nolo
gies
, suc
h as
mob
ility
ser
vice
s
In a
typi
cal y
ear,
how
oft
en d
o yo
u us
e th
e fo
llow
ing
tech
nolo
gies
and
ser
vice
s? P
leas
e co
nsid
er a
typi
cal y
ear,
for i
nsta
nce
2019
, bef
ore
the
Covi
d-19
pan
dem
ic st
arte
d. P
leas
e se
lect
one
ans
wer
in e
ach
row
.
Questionnaire
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B1.
Exp
ress
del
iver
y
Hav
e no
t use
d ye
t,
but w
ould
con
side
r it
A fe
w ti
mes
a
year
6 to
10
tim
es a
ye
arEv
ery
2 w
eeks
Ever
y w
eek
or
mor
e of
ten
Nev
er u
sed
and
not i
nter
este
dO
nce
a m
onth
In a
typi
cal y
ear,
how
oft
en d
o yo
u sh
op o
nlin
e fo
r you
rsel
f or y
our f
amily
(suc
h as
for f
ood,
bev
erag
es, d
aily
sup
plie
s, c
onsu
mer
ele
ctro
nics
, fas
hion
and
app
arel
) and
use
exp
ress
del
iver
y fo
r you
r pur
chas
es?
Plea
se
cons
ider
a ty
pica
l yea
r, fo
r ins
tanc
e, 2
019,
bef
ore
the
Covi
d-19
pan
dem
ic st
arte
d.
B1.
Fre
quen
cy o
f usa
ge o
f exp
ress
del
iver
y se
rvic
es
129Questionnaire
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C1 &
C2.
Flig
ht h
abit
s
C1. F
requ
ency
of t
rave
lling
by
aero
plan
e
Not
at a
ll1-
2 ti
mes
a y
ear
3-5
tim
es a
yea
r6-
10 ti
mes
a y
ear
Ever
y m
onth
or m
ore
ofte
n
C2. M
ode
of tr
ansp
ort t
o ai
rpor
t
In a
typi
cal y
ear,
how
oft
en d
o yo
u tr
avel
by
aero
plan
efo
r per
sona
l or b
usin
ess
reas
ons?
Ple
ase
cons
ider
a ty
pica
l yea
r, fo
r ins
tanc
e 20
19, b
efor
e th
e Co
vid-
19 p
ande
mic
sta
rted
. Ple
ase
sele
ct o
ne a
nsw
er.
Whe
n tr
avel
ling
by a
erop
lane
, how
do
you
typi
cally
get
to a
nd fr
om th
e ai
rpor
t fro
m y
our h
ome
and
vice
ver
sa?
Plea
se s
elec
t all
that
app
ly.
By fo
ot, o
wn
(e-)
bike
, or o
wn
(e-)
scoo
ter
By (e
-)bi
ke o
r (e-
)sco
oter
from
a s
harin
g pr
ovid
er, s
uch
as L
ime,
Tie
r, e
mm
y, C
itySc
oot,
Dot
t, G
oVol
t, B
ird, V
aim
oo, M
OL
bubi
, ogr
e&co
1
Priv
ate
vehi
cle,
suc
h as
a c
ar o
r mot
orbi
ke
Taxi
or e
quiv
alen
t pro
vide
r, s
uch
as F
ree
Now
, Ube
r, B
olt,
it T
axi,
Cabi
fy, M
oove
, Vig
go1
Cars
harin
g, su
ch a
s Sha
re N
ow, M
iles,
Cam
bio,
get
arou
nd, U
beeq
o, e
njoy
, let
sgo,
Gre
enM
obili
ty, M
OL
Lim
o, G
reen
Go1
Ride
shar
ing,
such
as U
ber P
ool o
r Moi
a or
oth
er lo
cal r
ides
hare
opt
ion1
Publ
ic tr
ansp
ort,
such
as b
us, m
etro
, or t
rain
Oth
er, p
leas
e sp
ecify
: ___
____
____
____
____
____
____
__
1.O
nly
rele
vant
pro
vide
rs in
resp
ectiv
e ci
ties
are
show
n
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S5. G
ener
al a
ttit
ude
tow
ards
urb
an a
ir m
obili
ty
Wha
t wou
ld b
e yo
ur o
vera
ll pe
rcep
tion
if ur
ban
air m
obili
ty s
olut
ions
(suc
h as
thos
e sh
own
in th
e vi
deo1 ) w
ere
to b
e in
trod
uced
in y
our c
ity?
Plea
se s
elec
t one
ans
wer
.
S5. R
atin
g of
per
cept
ion
from
ver
y ne
gativ
e to
ver
y po
sitiv
e
nega
tive
rath
er n
egat
ive
rath
er p
osit
ive
very
pos
itiv
e
1.To
info
rm p
artic
ipan
ts a
bout
urb
an a
ir m
obili
ty, a
vid
eo o
f len
gth
1:34
min
utes
was
show
n pr
ior t
o th
is q
uest
ion,
show
casi
ng th
eus
e ca
ses
pass
enge
r tra
nspo
rt b
y ai
r tax
i (po
intin
g ou
t to
the
exis
tenc
e of
man
ned
and
unm
anne
d V
TOLs
), pa
rcel
del
iver
y vi
a dr
one
(mor
e pr
ecis
ely,
inst
ant f
ood
deliv
ery)
, tra
nspo
rt o
f em
erge
ncy
med
ical
per
sonn
el to
site
of a
n ac
cide
nt, a
nd d
eliv
ery
of m
edic
al s
uppl
y to
a h
ospi
tal
131Questionnaire
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S6.S
7. L
ikel
ihoo
d to
try
out u
rban
air
mob
ility
ser
vice
s
How
like
ly a
re y
ou to
mak
e us
e of
del
iver
y of
goo
ds b
y dr
one
(i.e.
, del
iver
y of
par
cels
from
an
onlin
e sh
oppi
ng p
latf
orm
to a
near
by d
eliv
ery
hub,
you
r gar
den
or p
rivat
e pr
oper
ty o
r a p
ublic
ly a
cces
sibl
e ar
ea),
if it
wer
e of
fere
d in
you
r city
? Pl
ease
ass
ume
that
del
iver
y by
dro
ne w
ould
cos
t abo
ut d
oubl
e to
day’
s st
anda
rd s
hipp
ing
fees
and
ensu
red
guar
ante
ed d
eliv
ery
with
in 2
hou
rs fr
om th
e tim
e yo
u pl
ace
your
or
der.
S6. L
ikel
ihoo
d to
try
out d
rone
del
iver
y
nega
tive
rath
er n
egat
ive
rath
er p
osit
ive
very
pos
itiv
e
How
like
ly w
ould
you
be
to u
se a
n ai
r tax
i (i.e
., a
flyin
g ve
hicl
e th
at tr
ansp
orts
pas
seng
ers f
rom
A to
B) f
or a
25-
50%
hig
her p
rice
than
cur
rent
road
pas
seng
er tr
ansp
ort o
ptio
ns li
ke c
onve
ntio
nal (
road
) tax
is o
r U
ber-
like
offe
rings
, if y
ou a
ssum
e th
e tr
ip c
ould
be
mad
e in
hal
f the
tim
e in
the
air t
axi?
S7. L
ikel
ihoo
d to
try
out a
ir ta
xis
nega
tive
rath
er n
egat
ive
rath
er p
osit
ive
very
pos
itiv
e
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A3.
A2.
Per
ceiv
ed u
sefu
lnes
s of
urb
an a
ir m
obili
ty u
se c
ases
, am
ong
and
acro
ss c
ateg
orie
s
A2.
a R
anki
ng o
f use
cas
es in
ca
tego
ry d
rone
del
iver
yA
2.b
Ran
king
of u
se c
ases
in
cate
gory
med
ical
em
erge
ncy
A2.
c R
anki
ng o
f use
cas
es in
ca
tego
ry p
asse
nger
tran
spor
t
Whi
ch o
f the
bel
ow d
eliv
ery
use
case
s wou
ld y
ou
cons
ider
the
mos
t use
ful?
Ple
ase
sort
the
follo
win
g ap
plic
atio
ns fr
om 1
bei
ng ‘m
ost u
sefu
l’ to
4 b
eing
‘lea
st u
sefu
l’ or
sel
ect ‘
none
of t
hese
ar
e us
eful
’.1
Whi
ch o
f the
bel
ow m
edic
al e
mer
genc
y us
e ca
ses w
ould
you
con
side
r the
mos
t use
ful i
n an
ur
ban
envi
ronm
ent?
Ple
ase
sort
the
follo
win
g ap
plic
atio
ns fr
om 1
bei
ng ‘m
ost u
sefu
l’ to
5 b
eing
‘le
ast u
sefu
l’ or
sel
ect ‘
none
of t
hese
are
use
ful’.
1
Whi
ch o
f the
bel
ow p
asse
nger
tran
spor
t use
ca
ses f
or u
rban
air
mob
ility
wou
ld y
ou c
onsi
der
the
mos
t use
ful?
Ple
ase
sort
the
follo
win
g ap
plic
atio
ns fr
om 1
bei
ng ‘m
ost u
sefu
l’ to
5 b
eing
‘le
ast u
sefu
l’ or
sel
ect ‘
none
of t
hese
are
use
ful’.
1
And
whi
ch o
f the
bel
ow u
se c
ases
(tha
t you
pr
evio
usly
sel
ecte
d as
the
mos
t use
ful i
n th
eir
cate
gorie
s) a
re th
e m
ost u
sefu
l ove
rall?
Ple
ase
sort
the
follo
win
g ap
plic
atio
ns fr
om ‘m
ost u
sefu
l’ to
‘lea
st u
sefu
l’ or
sel
ect ‘
none
of t
hese
are
us
eful
’.1
A3.
Cro
ss-c
ateg
ory
rank
ing
of to
p 2
use
case
s fr
om c
ateg
orie
s3
Non
e of
thes
e ar
e us
eful
Non
e of
thes
e ar
e us
eful
Non
e of
thes
e ar
e us
eful
Dro
ne d
eliv
ery
of g
oods
in a
n ur
ban
area
, fo
r ins
tanc
e, fr
om m
y pr
efer
red
onlin
e sh
oppi
ng p
orta
l or s
ite
Dro
ne d
eliv
ery
of m
eals
in a
n ur
ban
area
, fo
r ins
tanc
e, fr
om m
y pr
efer
red
rest
aura
nt
or fo
od d
eliv
ery
site
Gro
cery
and
goo
ds d
eliv
ery
to m
y ho
me
or
wor
kpla
ce in
are
as w
ith lo
ng tr
avel
tim
es to
th
e ne
xt s
hop
(i.e.
, in
the
coun
trys
ide
or n
ot
wel
l con
nect
ed w
ith p
ublic
tran
spor
t)
Long
-dis
tanc
e fo
rwar
ding
of h
eavy
car
go,
for i
nsta
nce,
to p
lace
s with
litt
le
infr
astr
uctu
re su
ch a
s isl
ands
or d
rillin
g rig
s
Dro
ne d
eliv
ery
of m
edic
al s
uppl
ies
(suc
h as
bl
ood
dona
tions
, org
ans,
med
ical
eq
uipm
ent)
to a
hos
pita
l
Dro
ne d
eliv
ery
of m
edic
al s
uppl
ies
for
pers
onal
nee
d (s
uch
as p
ens f
or a
uto-
inje
ctio
n of
a v
ital s
ubst
ance
, def
ibril
lato
rs
and
med
icat
ions
) fro
m a
cen
tral
hub
to a
pl
ace
not f
ar fr
om y
our h
ome
such
as
your
ow
n ga
rden
or a
nea
rby
park
Dis
aste
r man
agem
ent u
sing
dro
nes (
such
as
dron
es w
ith th
erm
al c
amer
as to
eva
luat
e fir
e in
cide
nts,
dro
nes
equi
pped
with
fir
efig
htin
g fo
ams a
nd e
xtin
guis
her b
ombs
)
Emer
genc
y m
edic
al se
rvic
e to
tran
sfer
an
inju
red
pers
on to
a n
earb
y ho
spita
l
Emer
genc
y m
edic
al s
ervi
ce to
brin
g do
ctor
s or
firs
t aid
per
sonn
el to
the
scen
e of
an
acci
dent
Sigh
tsee
ing
by a
ir in
a c
ity o
r reg
ion
Air
taxi
, for
inst
ance
, to
get f
rom
the
city
ce
ntre
to th
e lo
cal a
irpor
t and
vic
e ve
rsa
Air
taxi
, for
inst
ance
, to
com
mut
e fr
om a
su
burb
an a
rea
to th
e ci
ty c
entr
e
Air
taxi
to tr
avel
from
one
poi
nt in
the
city
to
ano
ther
Regi
onal
air
mob
ility
, for
inst
ance
, fro
m o
ne
city
to a
noth
er s
mal
l city
in th
e sa
me
regi
on
(like
from
Cop
enha
gen
to M
alm
ö2 )
1. E
xam
ple
of re
gion
al a
ir m
obili
ty o
nly
show
n in
Öre
sund
, as o
nly
supr
a-ur
ban
area
whe
re su
rvey
was
con
duct
ed
2. A
nsw
ers w
ere
shuf
fled
to a
void
bia
ses
tow
ards
opt
ions
with
hig
her p
ositi
on 3
. If
“Non
e of
thes
e ar
e us
eful
” w
as se
lect
ed in
all
thre
e A
2 qu
estio
ns, A
3 is
ski
pped
4.
No
“Non
e” o
ptio
n in
A3,
as p
artic
ipan
t mus
t hav
e se
lect
ed a
n op
tion
he c
once
ived
as
usef
ul in
som
e A
2 qu
estio
n, o
ther
wis
e se
e 3.<I
f not
non
e, #
1 ra
nked
opt
ion
from
A2.
a is
di
spla
yed>
<If n
ot n
one,
#2
rank
ed o
ptio
n fr
om A
2.a
is
disp
laye
d>
<If n
ot n
one,
#1
rank
ed o
ptio
n fr
om A
2.b
is
disp
laye
d>
<If n
ot n
one,
#2
rank
ed o
ptio
n fr
om A
2.b
is
disp
laye
d>
<If n
ot n
one,
#1
rank
ed o
ptio
n fr
om A
2.c
is
disp
laye
d>
<If n
ot n
one,
#2
rank
ed o
ptio
n fr
om A
2.c
is
disp
laye
d>4
133133Questionnaire
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B6.
Lev
el o
f com
fort
wit
h dr
op-o
ff lo
cati
ons
for d
rone
del
iver
y
B6.
Sel
ecti
on o
f lev
el o
f com
fort
wit
h dr
op-o
ff lo
cati
ons
for d
rone
del
iver
yH
ow c
omfo
rtab
le w
ould
you
be
with
the
follo
win
g m
odes
of d
rone
del
iver
y fo
r med
ium
-siz
ed p
arce
ls (m
ax. 1
20 x
60
x 60
cm
, up
to 5
kg) a
t pla
ces n
ear y
our h
ome?
Ple
ase
sele
ct o
ne a
nsw
er in
eac
h ro
w
very
un
com
fort
able
unco
mfo
rtab
leso
mew
hat
unco
mfo
rtab
leso
mew
hat
com
fort
able
very
com
fort
able
com
fort
able
A. O
ver t
he w
alkw
ay in
fron
t of m
y ho
use
or m
ain
door
of m
y bl
ock
of fl
ats (
publ
icly
acc
essi
ble)
B. I
n m
y ga
rden
or o
ther
priv
ate
area
(not
pub
licly
acc
essi
ble)
C. O
n th
e ro
of o
f my
hous
e/bl
ock
of fl
ats o
r off
ice
D. I
n a
near
by p
ark
E. C
entr
al d
eliv
ery
stat
ion
in m
y ne
ighb
ourh
ood,
suc
h as
a p
etro
l st
atio
n, s
uper
mar
ket o
r a p
osta
l ser
vice
sta
tion
134 A study on the societal acceptance of Urban Air Mobility in Europe
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A4
& A
5. B
enef
its
incu
rred
by
UA
M
A4.
Sel
ecti
on o
f up
to 3
ben
efit
s in
curr
ed b
y U
AM
Wha
t ben
efits
and
opp
ortu
nitie
s can
the
deve
lopm
ent o
f urb
an a
ir m
obili
ty b
ring
for t
he E
U a
nd E
U
citiz
ens?
Ple
ase
sele
ct u
p to
3 a
nsw
ers.
A5.
Fur
ther
pos
itiv
e be
nefit
s m
enti
oned
by
surv
ey p
arti
cipa
nts
Wha
t oth
er b
enef
its a
nd o
ppor
tuni
ties
can
the
deve
lopm
ent o
f urb
an a
ir m
obili
ty b
ring
for E
U
citiz
ens?
Ple
ase
add
up to
3 id
eas
in th
e be
low
text
box
es.1,
2
Impr
oved
dev
elop
men
t of a
nd a
cces
s to
rem
ote
area
s (f
or in
stan
ce, t
he c
ount
rysi
de, r
egio
ns
outs
ide
of a
met
ropo
litan
are
a)
Crea
tion
of n
ew jo
bs a
nd g
row
th o
ppor
tuni
ties
for p
eopl
e in
my
coun
try
(for
inst
ance
, m
anuf
actu
ring,
rese
arch
and
dev
elop
men
t, p
ilot p
roje
cts)
Mar
ket-
lead
ing
posi
tion
of E
urop
e in
urb
an a
ir m
obili
ty te
chno
logy
(suc
h as
dro
nes,
air
taxi
s)
Redu
ctio
n of
traf
fic ja
ms
Redu
ctio
n of
loca
l em
issi
ons
and
pollu
tion
(mos
t of t
he v
ehic
les
will
hav
e ba
tter
y el
ectr
ic
prop
ulsi
on)
Redu
ced
resp
onse
tim
e fo
r em
erge
ncie
s
Non
e of
thes
e ar
e us
eful
____
____
____
____
____
____
____
_
____
____
____
____
____
____
____
_
____
____
____
____
____
____
____
_
1.Th
ree
free
text
fiel
ds w
ere
show
n to
sur
vey
part
icip
ants
2.Th
e ev
alua
tion
of fr
ee te
xt b
oxes
in q
uest
ion
A5.
did
not
sho
w o
ther
sign
ifica
nt b
enef
its; a
n ex
ampl
e ca
n be
foun
d in
resp
ectiv
e ev
alua
tion
for P
aris
135Questionnaire
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B2.
Ben
efit
s of
dro
ne d
eliv
ery
B2.
Sel
ecti
on o
f up
to 3
ben
efit
s in
curr
ed b
y dr
one
deliv
ery
Wha
t ben
efits
and
opp
ortu
nitie
s ca
n th
e de
velo
pmen
t of u
rban
air
mob
ility
brin
g fo
r the
EU
and
EU
citi
zens
? Pl
ease
sel
ect u
p to
3 a
nsw
ers.
Fast
er d
eliv
ery
Exac
t tim
e w
indo
w fo
r del
iver
y
Hig
her r
elia
bilit
y
Few
er in
tera
ctio
ns w
ith o
ther
peo
ple
(i.e.
for b
ette
r hyg
iene
sta
ndar
ds, e
spec
ially
dur
ing/
afte
r the
pan
dem
ic)
Less
-con
gest
ed s
tree
ts (i
.e. d
ue to
few
er d
eliv
ery
vehi
cles
par
ked
on th
e st
reet
)
Less
-con
gest
ed c
ity c
entr
es d
ue to
few
er p
eopl
e ou
t sho
ppin
g
Non
e of
thes
e ar
e us
eful
Less
pol
lutio
n du
e to
ele
ctric
pro
puls
ion
136 A study on the societal acceptance of Urban Air Mobility in Europe
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C3. B
enef
its
of a
ir ta
xis
C3. R
anki
ng o
f ben
efit
s in
curr
ed b
y pa
ssen
ger t
rans
port
wit
h ai
r tax
is
In y
our v
iew
, wha
t are
the
mai
n be
nefit
s of
air
taxi
s? P
leas
e so
rt th
e fo
llow
ing
bene
fits
from
the
1 be
ing
the
‘mos
t use
ful’
to 7
or 8
1be
ing
the
‘leas
t use
ful’
or s
elec
t ‘no
ne o
f the
se a
re u
sefu
l’.
Non
e of
thes
e ar
e us
eful
1.N
umbe
r of o
ptio
ns is
8 if
par
ticip
ants
opt
s for
filli
ng th
e fr
ee te
xt fi
eld
2.Th
e ev
alua
tion
of fr
ee te
xt b
oxes
did
not
sho
w o
ther
sign
ifica
nt b
enef
its
Sign
ifica
nt ti
me
savi
ng fo
r pas
seng
ers
Com
fort
able
con
tinua
tion
of tr
avel
aft
er a
rriv
al a
t an
airp
ort s
tatio
n to
my
pers
onal
acc
omm
odat
ion
Redu
ctio
n of
traf
fic ja
ms
Less
noi
se fo
r inn
er-c
ity re
side
nts
due
to e
mer
genc
y m
edic
al s
ervi
ces
taki
ng a
n ae
rial r
oute
Abi
lity
to c
onne
ct a
nd a
cces
s re
mot
e ar
eas
(are
as w
ith c
urre
nt p
oor a
cces
s to
city
cen
tres
)
The
feel
ing
of b
ehav
ing
in a
mod
ern
way
, bei
ng a
n ea
rly a
dopt
er
Crea
tion
of n
ew jo
bs in
runn
ing
air t
axi s
ervi
ces
Oth
er, p
leas
e sp
ecify
: ___
____
____
____
____
____
____
__2
137137Questionnaire
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B3.
Lev
el o
f com
fort
wit
h un
man
ned
deliv
ery
dron
es
B3.
Per
cept
ion
of s
afet
y w
ith
unm
anne
d de
liver
y dr
ones
Dro
nes
inte
nded
for t
he d
eliv
ery
of g
oods
are
rem
otel
y pi
lote
d ai
rcra
ft s
yste
ms w
ith n
o pi
lots
on
boar
d. A
ssum
e th
at th
ey h
ave
an a
vera
ge w
ings
pan
of 3
met
res,
wou
ld fl
y at
bet
wee
n 12
0 an
d 15
0 m
etre
sal
titud
e,
and
are
cert
ified
by
com
pete
nt a
utho
ritie
s. P
leas
e ra
te h
ow m
uch
you
agre
e or
dis
agre
e w
ith th
e fo
llow
ing
stat
emen
t.
stro
ngly
dis
agre
edi
sagr
eeso
mew
hat
disa
gree
som
ewha
t agr
eest
rong
ly a
gree
agre
e
As
a pe
dest
rian
on th
e gr
ound
, I w
ould
feel
saf
e w
ith u
nman
ned
deliv
ery
dron
es p
oten
tially
flyi
ng a
bove
me.
138 A study on the societal acceptance of Urban Air Mobility in Europe
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C4. L
evel
of c
omfo
rt w
ith
man
ned
& u
nman
ned
air t
axis
for p
asse
nger
s &
ped
estr
ians
C4. L
evel
of c
omfo
rt w
ith
air t
axis
by
leve
l of a
uton
omy
(pre
sent
ver
sus
abse
nt) a
nd d
egre
e of
inte
ract
ion
(act
ive
vers
us p
assi
ve)
Rece
nt s
tudi
es e
xten
d th
e pr
ospe
ct o
f airc
raft
soo
n tr
ansp
ortin
g pa
ssen
gers
, eith
er w
ith a
pilo
t on
boar
d or
with
a re
mot
e pi
lot.
You
will
now
see
sev
eral
sta
tem
ents
that
peo
ple
mig
ht m
ake
abou
t suc
h ai
r tax
is.
Ass
umin
g th
at a
ll of
the
airc
raft
are
cer
tifie
d by
com
pete
nt a
utho
ritie
s, p
leas
e ra
te h
ow m
uch
you
agre
e or
dis
agre
e w
ith e
ach
stat
emen
t for
eac
h ty
pe o
f air
taxi
.
stro
ngly
dis
agre
edi
sagr
eeso
mew
hat
disa
gree
som
ewha
t agr
eest
rong
ly a
gree
agre
e
I wou
ld b
e in
tere
sted
in tr
ying
out
the
follo
win
g ve
hicl
es m
ysel
f:
A. M
anne
d ai
r tax
i (m
eani
ng w
ith a
hum
an p
ilot o
n bo
ard
stee
ring
the
airc
raft
)
B. U
nman
ned
air t
axi (
mea
ning
no
hum
an p
ilot i
s on
boa
rd to
ste
er
the
airc
raft
)
As
a pe
dest
rian
(not
as
a pa
ssen
ger)
, I a
m o
kay
with
acc
ept t
he fa
ct
that
the
follo
win
g ve
hicl
es c
ould
fly
abov
e m
y he
ad
C. M
anne
d ai
r tax
i (m
eani
ng w
ith a
hum
an p
ilot o
n bo
ard
stee
ring
the
airc
raft
)
D. U
nman
ned
air t
axi (
mea
ning
no
hum
an p
ilot i
s on
boa
rd to
ste
er
the
airc
raft
)
139Questionnaire
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B4.
B5.
Abs
olut
e an
d re
lati
ve im
port
ance
of g
ener
al c
once
rns
in d
rone
del
iver
y us
e ca
se
1.A
nsw
ers w
ere
shuf
fled
to e
limin
ate
bias
rela
ted
to p
ositi
onin
g2.
Eval
uatio
n di
d no
t lea
d to
sign
ifica
nt o
ther
con
cern
.3.
Onl
y op
tions
cho
sen
in B
4 pr
ompt
ed in
B5;
if "N
one
of th
ese"
was
cho
sen
in B
4, q
uest
ion
B5 is
ski
pped
Wha
t are
you
mos
t con
cern
ed a
bout
rega
rdin
g dr
one
deliv
ery,
bot
h fo
r the
del
iver
y of
day
-to-
day
good
s as
wel
l as
med
ical
sup
plie
s? P
leas
e co
nsid
er y
our o
wn
usag
e of
suc
h a
serv
ice
as w
ell a
s ot
her p
eopl
e us
ing
it (e
.g. y
our f
amily
or n
eigh
bour
s), w
hich
may
aff
ect y
ou a
s wel
l. Pl
ease
sel
ect u
p to
6 a
nsw
ers.
1
B4.
Sel
ecti
on o
f up
to 6
con
cern
s
Non
e of
thes
e
Noi
se p
ollu
tion,
suc
h as
loud
and
/or a
nnoy
ing
soun
dsV
isua
l pol
lutio
n, su
ch a
s ann
oyin
g ai
r tra
ffic
Safe
ty c
once
rns,
such
as d
rone
s cra
shin
gCo
ncer
ns re
gard
ing
loca
l env
ironm
ent,
such
as a
ir po
llutio
n, n
egat
ive
impa
ct o
n bi
rd li
fe a
nd in
sect
s, o
r dec
reas
ing
biod
iver
sity
in g
ener
alG
loba
l env
ironm
enta
l con
cern
s, su
ch a
s neg
ativ
e im
pact
on
clim
ate
chan
geJo
b lo
sses
, for
inst
ance
with
in lo
cal d
eliv
ery
com
pani
esSe
curit
y th
reat
s, fo
r ins
tanc
e, c
rimin
al o
rgan
izat
ions
(for
rans
om),
hack
tivis
ts, o
r ter
roris
ts h
acki
ng in
to th
e co
ntro
l sys
tem
and
hija
ckin
g or
mis
dire
ctin
g dr
ones
Pr
ivac
y co
ncer
ns, f
or in
stan
ce, a
dro
ne fl
ying
clo
se to
my
win
dow
or o
ver m
y pr
oper
tyA
ffor
dabi
lity,
i.e.
the
serv
ice
bein
g af
ford
able
onl
y fo
r ric
h or
priv
ilege
d pe
ople
Oth
er, p
leas
e sp
ecify
: ___
____
____
____
_2
Plea
se s
ort y
our m
ain
conc
erns
from
‘mos
t con
cern
ing’
to ‘l
east
con
cern
ing’
.3
B5.
Ran
king
of p
revi
ousl
y se
lect
ed c
once
rns
<Con
cern
cho
sen
in B
4 is
dis
play
ed, i
f at l
east
1 w
as c
hose
n><C
once
rn c
hose
n in
B4
is d
ispl
ayed
, if a
t lea
st 2
wer
e ch
osen
><C
once
rn c
hose
n in
B4
is d
ispl
ayed
, if a
t lea
st 3
wer
e ch
osen
><C
once
rn c
hose
n in
B4
is d
ispl
ayed
, if a
t lea
st 4
wer
e ch
osen
><C
once
rn c
hose
n in
B4
is d
ispl
ayed
, if a
t lea
st 5
wer
e ch
osen
><C
once
rn c
hose
n in
B4
is d
ispl
ayed
, if 6
wer
e ch
osen
>
140 A study on the societal acceptance of Urban Air Mobility in Europe
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C5.C
6. A
bsol
ute
and
rela
tive
impo
rtan
ce o
f gen
eral
con
cern
s in
air
taxi
use
cas
e
1. A
nsw
ers w
ere
shuf
fled
to e
limin
ate
bias
rela
ted
to p
ositi
onin
g 2
. E
valu
atio
n di
d no
t lea
d to
sig
nific
ant o
ther
con
cern
3.
Onl
y op
tions
cho
sen
in B
4 pr
ompt
ed in
B5;
if "N
one
of th
ese"
was
cho
sen
in B
4, q
uest
ion
B5 is
ski
pped
4. "
Oth
er" o
ptio
n in
B5
cont
ains
text
inpu
t fro
m B
4, if
fille
d by
par
ticip
ant
Wha
t are
you
mos
t con
cern
ed a
bout
with
resp
ect t
o ai
r tax
is?
Plea
se c
onsi
der y
our o
wn
usag
e of
suc
h a
serv
ice
as w
ell a
s oth
er p
eopl
e us
ing
it (e
.g. y
our f
amily
or n
eigh
bour
s), w
hich
may
aff
ect y
ou a
s wel
l. Pl
ease
sel
ect u
p to
6 a
nsw
ers.
1
Plea
se s
ort y
our m
ain
conc
erns
from
‘mos
t con
cern
ing’
to ‘l
east
con
cern
ing’
.3,4
C5. S
elec
tion
of u
p to
6 c
once
rns
C6. R
anki
ng o
f pre
viou
sly
sele
cted
con
cern
s
Noi
se p
ollu
tion,
such
as l
oud
and/
or a
nnoy
ing
soun
ds o
f fly
ing
airc
raft
Visu
al p
ollu
tion,
such
as a
nnoy
ing
air t
raffi
c
Safe
ty co
ncer
ns, s
uch
as fl
ying
vehi
cles
pos
sibly
cras
hing
Conc
erns
rega
rdin
g th
e lo
cal e
nviro
nmen
t, su
ch a
s air
pollu
tion,
neg
ativ
e im
pact
on
bird
life
and
inse
cts,
or
dec
reas
ing
biod
iver
sity
in g
ener
al
Glo
bal e
nviro
nmen
tal c
once
rns,
such
as n
egat
ive
impa
ct o
n cl
imat
e ch
ange
Job
loss
, for
inst
ance
affe
ctin
g ta
xi d
river
s
Secu
rity
thre
ats,
for i
nsta
nce,
crim
inal
org
aniz
atio
ns (f
or ra
nsom
), ha
cktiv
ists,
or t
erro
rists
hac
king
into
th
e co
ntro
l sys
tem
and
hija
ckin
g or
misd
irect
ing
the
air t
axi
Priv
acy
conc
erns
, for
inst
ance
, an
air t
axi f
lyin
g cl
ose
to m
y w
indo
w o
r ove
r my
prop
erty
Affo
rdab
ility
, i.e
. the
serv
ice
bein
g af
ford
able
onl
y fo
r ric
h or
priv
ilege
d pe
ople
Add
ition
al tr
affic
from
/to ta
ke-o
ff st
atio
ns
Inne
r-ci
ty sp
ace
occu
patio
n du
e to
infra
stru
ctur
e re
quire
men
ts (t
ake-
off a
nd la
ndin
g st
atio
ns)
Noi
se re
late
d to
the
oper
atio
n of
take
-off
sta
tions
Dow
nwas
h, i.
e. d
ownw
ard
win
d ge
nera
ted
by th
e ro
tors
of a
ir ta
xis
whe
n fly
ing
or in
hov
er
mod
eSq
uand
erin
g of
pub
lic m
oney
to fi
nanc
e ne
w in
fras
truc
ture
and
air
taxi
tech
nolo
gy, i
nste
ad o
f im
prov
ing
exis
ting
publ
ic tr
ansp
ort a
nd in
fras
truc
ture
like
road
s an
d ra
il
Flig
ht sh
ame
(i.e.
my
soci
al re
puta
tion
wou
ld su
ffer
as a
resu
lt of
usi
ng a
ir ta
xis)
Oth
er, p
leas
e sp
ecify
: ___
____
____
____
_2
Non
e of
thes
e
<Con
cern
chos
en in
C5
is di
spla
yed,
if a
t lea
st 1
was
chos
en>
<Con
cern
chos
en in
C5
is di
spla
yed,
if a
t lea
st 2
wer
e ch
osen
><C
once
rn ch
osen
in C
5 is
disp
laye
d, if
at l
east
3 w
ere
chos
en>
<Con
cern
chos
en in
C5
is di
spla
yed,
if a
t lea
st 4
wer
e ch
osen
><C
once
rn ch
osen
in C
5 is
disp
laye
d, if
at l
east
5 w
ere
chos
en>
<Con
cern
chos
en in
C5
is di
spla
yed,
if a
t lea
st 6
wer
e ch
osen
>
141Questionnaire
8A0846_Report_Spreads_210518_Langversion.indd 1418A0846_Report_Spreads_210518_Langversion.indd 141 19.05.2021 12:57:0519.05.2021 12:57:05
C11.
C12.
Con
cern
s re
gard
ing
take
-off
and
land
ing
stat
ions
Ass
umin
g th
at a
take
-off
and
land
ing-
stat
ion
is c
lose
by
(und
er 5
0 m
etre
s), w
hat a
re y
ou m
ost
conc
erne
d ab
out?
Ple
ase
sele
ct u
p to
6 a
nsw
ers.
1
C11.
Sel
ecti
on o
f up
to 6
con
cern
sPl
ease
sor
t you
r mai
n co
ncer
ns fr
om ‘m
ost c
once
rnin
g’ to
‘lea
st c
once
rnin
g’.3,
4
C12.
Ran
king
of p
revi
ousl
y se
lect
ed c
once
rns
Noi
se o
rigin
atin
g fr
om th
e ta
ke-o
ff a
nd la
ndin
g m
anoe
uvre
sof a
ir ta
xis
Incr
ease
d nu
mbe
r of p
eopl
e w
alki
ng b
y
Incr
ease
d ro
ad tr
affic
to a
nd fr
om th
e ta
ke-o
ff a
nd la
ndin
g st
atio
n
Vis
ual p
ollu
tion,
i.e.
too
man
y ae
rial v
ehic
les
in m
y fie
ld o
f vie
w, f
or in
stan
ce, w
hen
I loo
k ou
t of
my
win
dow
Brea
ch o
f my
priv
acy
Incr
ease
d se
curit
y th
reat
s (su
ch a
s ter
roris
ts h
ijack
ing
an a
ir ta
xi a
nd le
ttin
g it
cras
h on
pur
pose
)
Safe
ty is
sues
(fea
r of a
n in
crea
sed
num
ber o
f air
taxi
s cr
ashi
ng)
Take
-off
stat
ions
taki
ng u
p sp
ace
need
ed fo
r ret
ail
Take
-off
stat
ions
taki
ng u
p sp
ace
othe
rwis
e av
aila
ble
for l
ivin
g or
recr
eatio
n, s
uch
as p
arks
Oth
er, p
leas
e sp
ecify
: ___
____
____
____
_2
Non
e of
thes
e
<Con
cern
cho
sen
in C
11 is
dis
play
ed, i
f at l
east
1 w
as c
hose
n>
1. A
nsw
ers w
ere
shuf
fled
to e
limin
ate
bias
rela
ted
to p
ositi
onin
g 2
. E
valu
atio
n di
d no
t lea
d to
sig
nific
ant o
ther
con
cern
3.
Onl
y op
tions
cho
sen
in C
11 p
rom
pted
in C
12; i
f "N
one
of th
ese"
was
cho
sen
in B
4, q
uest
ion
B5 is
ski
pped
4.
"O
ther
" opt
ion
in C
12 c
onta
ins
text
inpu
t fro
m B
4, if
fille
d by
par
ticip
ant
<Con
cern
cho
sen
in C
11 is
dis
play
ed, i
f at l
east
2 w
ere
chos
en>
<Con
cern
cho
sen
in C
11 is
dis
play
ed, i
f at l
east
2 w
ere
chos
en>
<Con
cern
cho
sen
in C
11 is
dis
play
ed, i
f at l
east
2 w
ere
chos
en>
<Con
cern
cho
sen
in C
11 is
dis
play
ed, i
f at l
east
2 w
ere
chos
en>
<Con
cern
cho
sen
in C
11 is
dis
play
ed, i
f 6 w
ere
chos
en>
142 A study on the societal acceptance of Urban Air Mobility in Europe
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B9.
Env
iron
men
tal c
once
rns
rega
rdin
g de
liver
y dr
ones
B9.
Ran
king
of 7
env
iron
men
tal c
once
rns
pote
ntia
lly in
curr
ed b
y de
liver
y dr
ones
Wha
t are
you
r gre
ates
t con
cern
s w
hen
it co
mes
to th
e po
ssib
le e
nviro
nmen
tal c
onse
quen
ces o
f dro
ne d
eliv
ery?
Ple
ase
sort
the
follo
win
g an
swer
s fr
om 1
bei
ng ‘m
ost c
once
rnin
g’ to
7 b
eing
‘lea
st c
once
rnin
g’ o
r se
lect
‘non
e of
thes
e’.
Air
pollu
tion
Noi
se p
ollu
tion,
for e
xam
ple,
regu
lar e
xpos
ure
to e
leva
ted
soun
d le
vels
that
pot
entia
lly h
ave
adve
rse
effe
cts
on h
uman
s or
oth
erliv
ing
orga
nism
s
Neg
ativ
e im
pact
on
bird
life
, ins
ects
and
oth
er fl
ying
ani
mal
s
Hig
h en
viro
nmen
tal a
nd c
limat
e im
pact
from
dro
ne o
pera
tion,
incl
udin
g po
wer
gen
erat
ion
(e.g
. ele
ctric
ity)
Hig
h en
viro
nmen
tal a
nd c
limat
e im
pact
from
the
man
ufac
turin
g of
dro
nes,
incl
udin
g ba
tter
y pr
oduc
tion
Hig
h en
viro
nmen
tal i
mpa
ct fr
om th
e di
spos
al o
f dro
nes
Seal
ed s
urfa
ces,
for e
xam
ple,
cov
erin
g so
il w
ith m
ater
ials
like
con
cret
e an
d st
one,
e.g
. for
take
-off
and
land
ing
pads
, pot
entia
lly re
duci
ng n
atur
al s
oil a
nd e
cosy
stem
func
tion
in th
e ar
ea c
once
rned
Non
e of
thes
e
143Questionnaire
8A0846_Report_Spreads_210518_Langversion.indd 1438A0846_Report_Spreads_210518_Langversion.indd 143 19.05.2021 12:57:0519.05.2021 12:57:05
C9. E
nvir
onm
enta
l con
cern
s re
gard
ing
air t
axis
Wha
t are
you
r gre
ates
t con
cern
sw
hen
it co
mes
to th
e po
ssib
le e
nvir
onm
enta
l con
sequ
ence
sof
air
taxi
s? P
leas
e so
rt th
e fo
llow
ing
answ
ers f
rom
1 b
eing
‘mos
t con
cern
ing’
to 7
bei
ng ‘l
east
con
cern
ing’
or
sele
ct ‘n
one
of th
ese’
.
C9. R
anki
ng o
f 7 e
nvir
onm
enta
l con
cern
s po
tent
ially
incu
rred
by
air t
axis
Air
pollu
tion
Noi
se p
ollu
tion,
for e
xam
ple,
regu
lar e
xpos
ure
to e
leva
ted
soun
d le
vels
that
pot
entia
lly h
ave
adve
rse
effe
cts
on h
uman
s or
oth
erliv
ing
orga
nism
s
Neg
ativ
e im
pact
on
bird
life
, ins
ects
and
oth
er fl
ying
ani
mal
s
Hig
h en
viro
nmen
tal a
nd c
limat
e im
pact
from
air
taxi
ope
ratio
n, in
clud
ing
pow
er g
ener
atio
n (e
.g. e
lect
ricity
)
Hig
h en
viro
nmen
tal a
nd c
limat
e im
pact
from
the
man
ufac
turin
g of
air
taxi
s, in
clud
ing
batt
ery
prod
uctio
n
Hig
h en
viro
nmen
tal i
mpa
ct fr
om th
e di
spos
al o
f air
taxi
s
Seal
ed s
urfa
ces,
for e
xam
ple,
cov
erin
g so
il w
ith m
ater
ials
like
con
cret
e an
d st
one,
e.g
. for
take
-off
and
land
ing
pads
, pot
entia
lly re
duci
ng n
atur
al s
oil a
nd e
cosy
stem
func
tion
in th
e ar
ea c
once
rned
Non
e of
thes
e
144 A study on the societal acceptance of Urban Air Mobility in Europe
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D4.
Intr
oduc
tion
of a
n ec
o-la
bel
Shou
ld th
e en
viro
nmen
tal i
mpa
ct o
f urb
an a
ir m
obili
ty o
pera
tions
be
eval
uate
d by
the
auth
oriti
es a
nd m
ade
publ
ic, e
.g.,
via
an e
co-la
bel s
uch
as th
e on
e sh
own
belo
w?
Plea
se s
elec
t one
ans
wer
.
D4.
Opi
nion
on
cert
ifica
tion
of e
nvir
onm
enta
l im
pact
of U
AM
A++
+
A++
A+
A B C D
Yes
, cer
tain
lyM
ay, n
ot s
ure
No
145Questionnaire
8A0846_Report_Spreads_210518_Langversion.indd 1458A0846_Report_Spreads_210518_Langversion.indd 145 19.05.2021 12:57:0619.05.2021 12:57:06
B7.
B8.
Tra
ding
off
con
cern
s sa
fety
, noi
se a
nd v
isua
l pol
luti
on re
gard
ing
dron
e de
liver
y
1.8
trad
e-of
fs p
er p
artic
ipan
t to
be m
ade
2.O
ther
pos
sibl
e le
vels
for s
afet
y, n
oise
ad
visu
al p
ollu
tion
on fo
llow
ing
slid
es
B7.
Rel
ativ
e im
port
ance
of a
ttri
bute
s sa
fety
, noi
se a
nd v
isua
l pol
luti
on in
con
join
t sty
le q
uest
ion
Put y
ours
elf i
n th
e ye
ar 2
030:
dro
nes
with
abo
ut 3
-met
re w
ings
pans
, cer
tifie
d by
com
pete
nt a
utho
ritie
s, a
re fl
ying
at a
ltitu
des
of u
p to
150
met
res.
In th
e fo
llow
ing
sect
ion,
you
will
be
aske
d w
hich
sce
nario
out
of
thre
e al
tern
ativ
es is
mos
t acc
epta
ble
from
you
r per
spec
tive.
Ple
ase
choo
se y
our m
ost p
refe
rred
opt
ion
out o
f the
thre
e al
tern
ativ
es s
how
n.1
Alt
erna
tive
1A
lter
nati
ve 2
Alt
erna
tive
3Ex
ampl
e ch
oice
car
d2
Safe
tyO
ne d
rone
has
the
sam
e lik
elih
ood
of h
ittin
g a
pede
stria
n as
one
car
One
dro
ne h
as 1
/100
th th
e lik
elih
ood
of h
ittin
g a
pede
stria
n as
one
car
One
dro
ne h
as 1
/100
th th
e lik
elih
ood
of h
ittin
g a
pede
stria
n as
one
car
Noi
seO
ne d
rone
is a
s lou
d as
a tr
uck
driv
ing
by a
t city
spe
ed
(~82
dB,
dis
turb
ing)
One
dro
ne is
as l
oud
as a
bic
ycle
ridi
ng b
y at
city
spe
ed
(~57
dB,
bar
ely
notic
eabl
e)O
ne d
rone
is a
s lou
d as
a c
ar d
rivin
g by
at c
ity s
peed
(~65
dB
, not
icea
ble)
Vis
ual p
ollu
tion
~10
dron
es p
er h
our i
n on
e’s
field
of v
iew
whe
n w
alki
ng
dow
n a
stre
et20
+ dr
ones
per
hou
r in
one’
s fie
ld o
f vie
w w
hen
wal
king
do
wn
a st
reet
~5 d
rone
s pe
r hou
r in
one’
s fie
ld o
f vie
w w
hen
wal
king
do
wn
a st
reet
B8.
Abs
olut
e ac
cept
ance
of b
est c
ase
and
wor
st c
ase
alte
rnat
ives
for d
eliv
ery
dron
eA
gain
, put
you
rsel
f in
the
year
203
0. H
ow a
ccep
tabl
e w
ould
you
find
the
follo
win
g sc
enar
ios f
or th
e fu
ture
? Pl
ease
rate
eac
h sc
enar
io b
ased
on
the
scal
e sh
own
belo
w.
Ver
y un
acce
ptab
leU
nacc
epta
ble
Som
ewha
t un
acce
ptab
leSo
mew
hat
unac
cept
able
Ver
y ac
cept
able
Acc
epta
ble
A. U
rban
air
mob
ility
whe
re
1) th
e ch
ance
of a
del
iver
y dr
one
cras
hing
ont
o a
pede
stria
n is
1/
100t
h th
at o
f a c
ar h
ittin
g a
pede
stria
n,
2) d
rone
s fly
ing
by h
ave
a si
mila
r noi
se le
vel a
s bic
ycle
s pa
ssin
g by
at
city
spe
ed, a
nd
3) o
ne o
r tw
o dr
ones
pas
s by
per h
our
B. U
rban
air
mob
ility
whe
re
1) th
e ch
ance
of a
del
iver
y dr
one
cras
hing
ont
o a
pede
stria
n is
5
times
hig
her t
han
that
of a
car
hitt
ing
a pe
dest
rian,
2) d
rone
s fly
ing
by h
ave
a si
mila
r noi
se le
vel a
s a le
af b
low
er, a
nd
3) m
ore
than
20
dron
es p
ass b
y pe
r hou
r
146 A study on the societal acceptance of Urban Air Mobility in Europe
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C7.C
8. T
radi
ng o
ff c
once
rns
safe
ty, n
oise
and
vis
ual p
ollu
tion
rega
rdin
g ai
r tax
is
1.8
trad
e-of
fs p
er p
artic
ipan
t to
be m
ade
2.O
ther
pos
sibl
e le
vels
for s
afet
y, n
oise
ad
visu
al p
ollu
tion
on fo
llow
ing
slid
es
C7. R
elat
ive
impo
rtan
ce o
f att
ribu
tes
safe
ty, n
oise
and
vis
ual p
ollu
tion
in c
onjo
int s
tyle
que
stio
nPu
t you
rsel
f in
the
year
203
0: a
ir ta
xis
with
win
gspa
ns o
f up
to 1
2 m
etre
s, c
ertif
ied
by c
ompe
tent
aut
horit
ies,
are
flyi
ng a
t alti
tude
s of
abo
ut 1
50 m
etre
s. In
the
follo
win
g se
ctio
n, y
ou w
ill b
e as
ked
whi
ch s
cena
rio o
ut
of th
ree
alte
rnat
ives
is m
ost a
ccep
tabl
e fr
om y
our p
ersp
ectiv
e. P
leas
e ch
oose
you
r mos
t pre
ferr
ed o
ptio
n ou
t of t
he th
ree
alte
rnat
ives
sho
wn.
1
Alt
erna
tive
1A
lter
nati
ve 2
Alt
erna
tive
3Ex
ampl
e ch
oice
car
d2
Safe
tyA
n ai
r tax
i is
as sa
fe fo
r pas
seng
ers
and
pede
stria
ns a
s a
car (
i.e.,
~2 fa
talit
ies
per b
illio
n pa
ssen
ger k
m)
An
air t
axi i
s as
safe
for p
asse
nger
s an
d pe
dest
rians
as a
co
mm
erci
al a
ircra
ft (i
.e.,
~0.0
1 fa
talit
ies
per b
illio
n pa
ssen
ger k
m)
An
air t
axi i
s as
safe
for p
asse
nger
s an
d pe
dest
rians
as a
bu
s (i.
e., ~
0.05
fata
litie
s pe
r bill
ion
pass
enge
r km
)
Noi
seA
n ai
r tax
i is
as lo
ud a
s a
car d
rivin
g by
at c
ity s
peed
(~65
dB
, not
icea
ble)
An
air t
axi i
s as
loud
as
a bi
cycl
e dr
ivin
g by
at c
ity s
peed
(~
57 d
B, b
arel
y no
ticea
ble)
An
air t
axi i
s as
loud
as
a tr
uck
driv
ing
by a
t city
spe
ed
(~82
dB,
dis
turb
ing)
Vis
ual p
ollu
tion
~10
air t
axis
per
hou
r in
one’
s fie
ld o
f vie
w w
hen
wal
king
do
wn
a st
reet
20+
air t
axis
per
hou
r in
one’
s fie
ld o
f vie
w w
hen
wal
king
do
wn
a st
reet
~5 a
ir ta
xis
per h
our i
n on
e’s
field
of v
iew
whe
n w
alki
ng
dow
n a
stre
et
C8. A
bsol
ute
acce
ptan
ce o
f bes
t cas
e an
d w
orst
cas
e al
tern
ativ
es fo
r del
iver
y dr
one
Aga
in, p
ut y
ours
elf i
n th
e ye
ar 2
030.
How
acc
epta
ble
wou
ld y
ou fi
nd th
e fo
llow
ing
scen
ario
s for
the
futu
re?
Plea
se ra
te e
ach
scen
ario
, bas
ed o
n th
e sc
ale
show
n be
low
.
Ver
y un
acce
ptab
leU
nacc
epta
ble
Som
ewha
t un
acce
ptab
leSo
mew
hat
unac
cept
able
Ver
y ac
cept
able
Acc
epta
ble
A. U
rban
air
mob
ility
whe
re
1) th
e sa
fety
stan
dard
for a
ir ta
xis i
s si
mila
r to
the
safe
ty st
anda
rd o
f co
mm
erci
al a
ircra
ft fo
r pas
seng
ers a
nd p
edes
tria
ns (h
ighe
st s
afet
y st
anda
rd),
2) a
ir ta
xis f
lyin
g by
hav
e a
sim
ilar n
oise
leve
l as
bicy
cles
pas
sing
by
at c
ity s
peed
s, a
nd
3) o
ne o
r tw
o ai
r tax
is p
ass b
y pe
r hou
r
B. U
rban
air
mob
ility
whe
re
1) th
e ris
k po
sed
by a
ir ta
xis f
or p
asse
nger
s and
ped
estr
ians
is
com
para
ble
to th
at o
f mot
orcy
cles
,
2) a
ir ta
xis f
lyin
g by
hav
e a
sim
ilar n
oise
leve
l as
leaf
blo
wer
s, a
nd
3) m
ore
than
20
air t
axis
pas
s by
per
hou
r
147Questionnaire
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B10
. Neg
ativ
e as
sum
ptio
ns a
bout
dro
ne d
eliv
ery
B10
. Agr
eem
ent w
ith
stat
emen
ts a
bout
dro
ne d
eliv
ery
pote
ntia
lly le
adin
g to
poo
r acc
epta
nce
To w
hat e
xten
t do
you
agre
e w
ith th
e fo
llow
ing
stat
emen
ts a
bout
dro
ne d
eliv
ery?
Ple
ase
rate
how
muc
h yo
u ag
ree
or d
isag
ree
with
eac
h of
the
follo
win
g st
atem
ents
Stro
ngly
dis
agre
eD
isag
ree
Som
ewha
t di
sagr
eeSo
mew
hat a
gree
Stro
ngly
agr
eeA
gree
A. I
sus
pect
that
I w
ould
find
ext
ra fe
es fo
r exp
ress
del
iver
y by
dro
ne
muc
h to
o ex
pens
ive.
B.I
wou
ld b
e af
raid
that
dro
nes
coul
d m
assi
vely
inva
de m
y pr
ivac
y,
for i
nsta
nce,
by
spyi
ng th
roug
h m
y w
indo
w a
nd re
cord
ing
my
pers
onal
life
.
C. I
wou
ld b
e af
raid
that
the
use
of d
rone
s fo
r the
del
iver
y of
goo
ds
wou
ld s
igni
fican
tly re
duce
the
num
ber o
f job
s, e
.g. f
or p
arce
l del
iver
y pe
rson
nel.
D. I
wou
ld b
e af
raid
that
the
dron
es c
ould
be
mis
used
and
bec
ome
a se
curit
y th
reat
.
Sour
ce: E
ASA
UA
M so
cial
acc
epta
nce
surv
ey q
uest
ions
C5.
Wha
t are
you
mos
t con
cern
ed a
bout
with
resp
ect t
o ai
r tax
is?
Plea
se c
onsi
der y
our o
wn
usag
e of
such
a
serv
ice
as w
ell a
s ot
her p
eopl
e us
ing
it (e
.g. y
our f
amily
or n
eigh
bour
s), w
hich
may
aff
ect y
ou a
s w
ell.
Plea
se se
lect
up
to 6
ans
wer
s. C
6. P
leas
e so
rt y
our m
ain
conc
erns
fr
om ‘m
ost c
once
rnin
g’ to
‘lea
st c
once
rnin
g’
148 A study on the societal acceptance of Urban Air Mobility in Europe
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C10.
Neg
ativ
e as
sum
ptio
ns a
bout
air
taxi
s &
ver
tipo
rts
To w
hat e
xten
t do
you
agre
e w
ith th
e fo
llow
ing
stat
emen
ts a
bout
aer
ial v
ehic
les?
Ple
ase
rate
how
muc
h yo
u ag
ree
or d
isag
ree
with
each
of t
he fo
llow
ing
stat
emen
ts.
C10.
Agr
eem
ent w
ith
stat
emen
ts a
bout
air
taxi
s po
tent
ially
lead
ing
to p
oor a
ccep
tanc
e
stro
ngly
dis
agre
edi
sagr
eeso
mew
hat
disa
gree
som
ewha
t agr
eest
rong
ly a
gree
agre
e
A. I
sus
pect
that
taki
ng a
n ai
r tax
i is
muc
h to
o ex
pens
ive
for m
e pe
rson
ally
.
B. I
sus
pect
that
onl
y ric
h pe
ople
will
be
able
to a
ffor
d ta
king
air
taxi
s.
C. A
s a
resi
dent
of t
he c
ity, I
wou
ld b
e af
raid
that
air
taxi
s or t
heir
pass
enge
rs c
ould
mas
sive
ly in
vade
my
priv
acy
whe
n fly
ing
over
my
hous
e/fla
t, fo
r ins
tanc
e, b
y sp
ying
thro
ugh
my
win
dow
and
re
cord
ing
my
pers
onal
life
.
D. I
am
afr
aid
that
the
intr
oduc
tion
of a
ir ta
xis s
igni
fican
tly re
duce
s th
e nu
mbe
r of j
obs,
aff
ectin
g, fo
r ins
tanc
e, ta
xi d
river
s.
149Questionnaire
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C10.
Neg
ativ
e as
sum
ptio
ns a
bout
air
taxi
s &
ver
tipo
rts
To w
hat e
xten
t do
you
agre
e w
ith th
e fo
llow
ing
stat
emen
ts a
bout
aer
ial v
ehic
les?
Ple
ase
rate
how
muc
h yo
u ag
ree
or d
isag
ree
with
each
of t
he fo
llow
ing
stat
emen
ts.
C10.
Agr
eem
ent w
ith
stat
emen
ts a
bout
ver
tipo
rts
pote
ntia
lly le
adin
g to
poo
r acc
epta
nce
stro
ngly
dis
agre
edi
sagr
eeso
mew
hat
disa
gree
som
ewha
t agr
eest
rong
ly a
gree
agre
e
E. T
ake-
off a
nd la
ndin
g st
atio
ns fo
r air
taxi
s ope
rate
d by
onl
y on
e pr
ovid
er (c
ompa
rabl
e to
airp
orts
for a
sin
gle
airli
ne o
r doc
ks fo
r a
sing
le fe
rry
line)
wou
ld h
ampe
r com
petit
ion
and
are
ther
efor
e ex
trem
ely
unfa
ir fr
om a
soc
ieta
l per
spec
tive.
F. I
cons
ider
the
allo
catio
n of
inne
r-ci
ty s
pace
for t
ake-
off a
nd
land
ing
stat
ions
of a
eria
l veh
icle
s as
com
plet
ely
unfa
ir or
un
nece
ssar
y.
G. I
wou
ld fe
el c
omfo
rtab
le li
ving
clo
se to
a ta
ke-o
ff s
tatio
n fo
r ae
rial v
ehic
les,
for i
nsta
nce,
with
in a
rang
e of
50
met
res.
150 A study on the societal acceptance of Urban Air Mobility in Europe
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D2.
Tru
st in
VTO
L se
curi
ty
To w
hat e
xten
t do
you
trus
t tha
t adv
ance
d ai
rcra
ft fl
ying
in a
n ur
ban
envi
ronm
ent w
ill b
e te
chno
logi
cally
sec
ure
and
arm
ed a
gain
st th
reat
s fr
om h
acke
rs (s
uch
as c
rimin
al o
rgan
isat
ions
, hac
ktiv
ists
or
terr
oris
ts) i
n th
e fo
llow
ing
case
s? P
leas
e se
lect
one
ans
wer
per
row
.
D2.
Lev
el o
f tru
st in
sec
urit
y sy
stem
s of
dro
nes
and
air t
axis
A. D
rone
s (s
uch
as d
eliv
ery
dron
es)
Secu
rity
B. A
ir ta
xis
Mis
trus
tSo
mew
hat
mis
trus
tSo
mew
hat t
rust
Trus
tFu
lly tr
ust
Fully
mis
trus
t
151Questionnaire
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D1.
Tru
st in
regu
lato
ry a
utho
riti
es
To w
hat e
xten
t do
you
trus
t the
follo
win
g au
thor
ities
to h
andl
e th
e ris
ks a
nd a
dopt
regu
latio
ns n
eede
d to
man
age
urba
n ai
r mob
ility
(inc
ludi
ng s
afet
y, n
oise
con
trol
, env
ironm
enta
l pro
tect
ion,
sec
urity
, cy
bers
ecur
ity, e
tc.)?
Ple
ase
sele
ct o
ne a
nsw
er p
er ro
w.
D1.
Lev
el o
f tru
st in
regu
lato
ry a
utho
riti
es b
y le
vel b
y ar
ea o
f inf
luen
ce
Secu
rity
A.E
urop
ean
auth
oriti
es
B.N
atio
nal a
utho
ritie
s
C.Re
gion
al o
r loc
al a
utho
ritie
s
Mis
trus
tSo
mew
hat
mis
trus
tSo
mew
hat t
rust
Trus
tFu
lly tr
ust
Fully
mis
trus
t
152 A study on the societal acceptance of Urban Air Mobility in Europe
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D3.
Impa
ct o
f re
gula
tion
on
trus
t
Wou
ld y
our t
rust
incr
ease
if th
e fo
llow
ing
regu
lato
rs w
ere
to d
evel
op re
gula
tions
to m
anag
e cy
bers
ecur
ity ri
sks
(cer
tific
atio
n an
d op
erat
ion
of a
eria
l veh
icle
s)?
Plea
se s
elec
t one
ans
wer
per
row
.
D3.
Dir
ecti
on a
nd d
imen
sion
of i
mpa
ct fr
om re
gula
tory
mea
sure
s on
trus
t lev
els
Trus
tD
ecre
ase
slig
htly
Stay
the
sam
eIn
crea
sesl
ight
lyIn
crea
seIn
crea
se a
lot
Dec
reas
e a
lot
A. E
urop
ean
auth
oriti
es
B. N
atio
nal a
utho
ritie
s
C. R
egio
nal o
r loc
al a
utho
ritie
s
153Questionnaire
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Appendix 3 Bibliography
UAM-related bibliography
Aaronson, M. et al. (2018, June 7). The aerospace industry isn’t ready for flying cars. Retrieved from https://www.bcg.com/de-de/publications/2018/aerospace-industry-is-not-ready-for-flying-cars.
ADAC Luftrettung. (2020, October 14). Multicopter in the rescue service. Retrieved from https://press.volocopter.com/images/pdf/ADAC-Luftrettung-Volocopter-feasibility-study-2020.pdf.
ADS Group. (2020, December 14). Whitepaper: Urban air mobility outlook. Retrieved from https://www.farnboroughinternational.org/Media/1863/urban-air-mobility-outlook-2020-whitepaper.pdf.
Airborne Drones. (2020, January 13). Drone noise levels. Retrieved from https://www.airbornedrones.co/drone-noise-levels/.
Al Haddad, C. et al. (2019, July 10). Towards understanding user adoption of urban air mobility. Retrieved from https://transp-or.epfl.ch/heart/2019/abstracts/hEART_2019_paper_123.pdf.
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Dietrich, A.M. (2020, January 1). Community benefits of urban air mobility (UAM). Retrieved from https://static1.squarespace.com/static/5d27bb3e330ac30001dc14fd/t/5e9cc83d4ee1eb456087402d/1587333191886/BenefitsofUAM_CAMI.pdf.
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Jones, S. (2020, November 5). Spain announces plans for flying taxi service in Barcelona. Retrieved from https://www.theguardian.com/world/2020/nov/05/spain-announces-plans-flying-taxi-service-barcelona.
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161Appendix 3
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