To investigate perceptions of citizens towards public...
Transcript of To investigate perceptions of citizens towards public...
I
To investigate perceptions of citizens towards public smart
parking systems in Birmingham area
A study submitted in partial fulfillment of the requirements for the
degree of Master of Science Information Management
at
THE UNIVERSITY OF SHEFFIELD
by
Chuxiong Zeng
September 2014
II
Acknowledgement
This work is indebted to a number of people. First of all, I take this opportunity to
express my profound gratitude and deep regards to my dissertation supervisor Dr.
Alex Peng for his constant help and guidance my research. Secondly, I would like to
thank my friends in information school, the University of Sheffield. Sharing and
exchanging some suggestions about research methodology on this research work.
Moreover, I would like to thank participators who helped me accomplish this research.
I am particularly grateful to my parents for their support and encouragement along my
postgraduate study.
Once again, many thanks all the people helped my research.
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Abstract
Background
Through reviewing relevant literature of smart cities and smart parking systems,
researcher found that previous researches concentrated on developing and assessing
smart parking systems. However, few study discuss people's opinion on smart parking
systems.
Aims
The purpose of this study is to investigate perceptions of people towards public smart
parking systems.
Methods
This study applied deductive approach and quantitative methodology. A questionnaire
was developed and implemented in Birmingham area. A number of questionnaires
have been collected as well as collected data are analyzed by some data analysis
methods.
Results
This research proposed theory that people hold a positive attitude towards public
smart parking systems has been proved. Some findings can support this argument.
Firstly, people are willing to use smart parking system to solve parking problems.
Secondly, people consider smart parking systems are useful, indicating in reducing
parking time and quickly pay parking fee. Moreover, smart parking systems help to
develop parking policies. The important is that people agree that smart parking
systems will be widely developed in the future. On the other hand, this research found
people's income and average parking time factor could affect their opinions of smart
parking systems. For instance, higher incomes people are more willing to use smart
parking systems as well as longer average parking time people prefer to use smart
parking systems. In addition, people think the government support and cost are the
determining factors of smart parking system development.
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Conclusions
Although the theory of this study is confirmed, more insight and comprehensive
researches relate to people’s perceptions of smart parking systems are expected.
Future work concerns the comparison of different areas related to people's perceptions
of smart parking systems as well as more powerful data analysis techniques are
desired.
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Table of Contents
Acknowledgement ........................................................................................................... II
Abstract ........................................................................................................................... III
Contents of Figure List ................................................................................................ VII
Contents of Table List ................................................................................................. VIII
Chapter 1 Introduction ................................................................................................... 1 1.1 Research Background ................................................................................................. 1 1.2 Research Aim and Objectives ..................................................................................... 2 1.3 Methodology .............................................................................................................. 2 1.4 Thesis Structure.......................................................................................................... 3
Chapter 2 An overview of Smart Cities ....................................................................... 5 2.1 Introduction................................................................................................................. 5 2.2 The Evolution of Smart Cities ..................................................................................... 5 2.3 Smart Cities in the World ............................................................................................ 6 2.4 Key areas of Smart Cities ............................................................................................ 7 2.5 Summary..................................................................................................................... 8
Chapter 3 An overview of Smart Parking Systems.................................................... 9 3.1 Introduction................................................................................................................. 9 3.2 Types of Smart Parking Systems (SPS) ....................................................................... 9
3.2.1 Parking Guidance Information Systems (PGI) ........................................................................ 9 3.2.2 Transit-Based Information Systems .......................................................................................... 10 3.2.3 Smart Payment Systems ................................................................................................................. 11 3.2.4 Automated Parking Systems ........................................................................................................ 12 3.2.5 E-Parking .............................................................................................................................................. 13
3.3 Smart Parking System Technologies .......................................................................... 15 3.3.1 RFID ....................................................................................................................................................... 15 3.3.2 Wireless Sensor Networks ............................................................................................................ 15
3.4 The Benefits of Smart Parking Systems ..................................................................... 16 3.5 Public Smart Parking System Programs in the World ................................................. 17
3.5.1 Smart Parking Systems in US ...................................................................................................... 17 3.5.2 Smart Parking Systems in China ................................................................................................ 17 3.5.3 Smart Parking Systems in UK ..................................................................................................... 18
3.6 Related Studies.......................................................................................................... 19 3.6.1 PGI system ........................................................................................................................................... 19 3.6.2 Transit-Based Information System ............................................................................................ 19 3.6.3 Smart Payment System ................................................................................................................... 19
3.7 Hypotheses development ........................................................................................... 20 3.7.1 The Impact of Personal Characteristic on Parking Issues ................................................ 20 3.7.2 Potential Correlations between Parking Behaviors............................................................. 21
3.8 Summary................................................................................................................... 22
Chapter 4 Research Methodology .............................................................................. 23 4.1 Introduction............................................................................................................... 23 4.2 Discussion of Research Methodologies ...................................................................... 23
4.2.1 Inductive Versus Deductive Research Method .................................................................... 23 4.2.2 Qualitative Versus Quantitative Research Method ............................................................. 24
4.3 Survey Design ........................................................................................................... 25 4.4 Data Collection ......................................................................................................... 26 4.5 Data Analysis Methods.............................................................................................. 27
4.5.1 Descriptive Analysis ........................................................................................................................ 27
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4.5.2 Analysis of Variance ....................................................................................................................... 28 4.5.3 Correlational Analysis .................................................................................................................... 30
4.6 Summary................................................................................................................... 31
Chapter 5 Findings and Discussions ........................................................................... 32 5.1 Introduction............................................................................................................... 32 5.2 Descriptive Analysis ................................................................................................. 32
5.2.1 Characteristics of Respondents ................................................................................................... 32 5.2.2 People's attitudes towards Smart Parking Systems ............................................................. 37 5.2.3 Benefits and Drawbacks of Smart parking systems ........................................................... 43
5.3 Analysis of Variance ................................................................................................. 46 5.3.1 The Impact of personal characteristics on willingness of using SPS .......................... 46 5.3.2 The Impact of personal characteristics on opinion of difficult parking ..................... 50
5.4 Correlation Analysis .................................................................................................. 51 5.5 Discussion ................................................................................................................. 54 5.6 Summary................................................................................................................... 56
Chapter 6 Conclusion ................................................................................................... 57 6.1 Introduction............................................................................................................... 57 6.2 Respond to Research Questions and Objectives ......................................................... 57 6.3 Limitations in This Research ..................................................................................... 58 6.4 Recommendations for Future Work ........................................................................... 58 6.5 Conclusions............................................................................................................... 59
References ....................................................................................................................... 60
Appendix A ..................................................................................................................... 72
Appendix B ..................................................................................................................... 77
Appendix C ..................................................................................................................... 78
Appendix D ..................................................................................................................... 81
VII
Contents of Figure List
Figure 1 PGI system ............................................................................................................................. 10
Figure 2 Transit based information system ................................................................................. 11
Figure 3 Smart payment system ...................................................................................................... 12
Figure 4 Automated parking system .............................................................................................. 13
Figure 5 Smart parking system ........................................................................................................ 14
Figure 6 Proposed research hypotheses model .......................................................................... 22
Figure 7 Gender proportion ............................................................................................................... 33
Figure 8 Age proportion ..................................................................................................................... 33
Figure 9 Income distribution ............................................................................................................. 34
Figure 10 Driving age proportion ................................................................................................... 35
Figure 11 Average parking time distribution .............................................................................. 35
Figure 12 Usage rate of smart parking systems ......................................................................... 36
Figure 13 User satisfaction of smart parking systems ............................................................. 37
Figure 14 Function demands for smart parking systems ........................................................ 43
Figure 15 Factors of hindering smart parking systems development ................................ 45
Figure 16 Proved research hypotheses model ............................................................................ 56
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Contents of Table List
Table 1 People’s opinions of smart parking systems ............................................................... 37
Table 2 Comparing different driving age opinion of difficult parking ............................. 38
Table 3 Comparing different average parking time opinion of difficult parking ......... 39
Table 4 Comparing different income opinion of difficult parking ..................................... 39
Table 5 Comparing different income opinion of willingness ............................................... 40
Table 6 Comparing different average parking time opinion of willingness ................... 40
Table 7 Comparing male and female opinions of willingness and usefulness .............. 41
Table 8 Comparing different ages opinion of using smartphone way .............................. 42
Table 9 People’s opinions of potential benefits of smart parking systems ..................... 44
Table 10 People’s opinions of drawbacks of smart parking systems ................................ 44
Table 11 Test of Homogeneity of variances (willingness) .................................................... 46
Table 12 Gender variable affects the willingness of using SPS .......................................... 47
Table 13 Income variable affects the willingness of using SPS .......................................... 48
Table 14 Driving age variable affects the willingness of using SPS ................................. 49
Table 15 Average parking time variable affects the willingness of using SPS ............. 49
Table 16 Test of Homogeneity of variances (difficult parking) .......................................... 50
Table 17 The Impact of personal characteristics on opinion of difficult parking ......... 51
Table 18 Correlation between difficult parking and willingness of using SPS ............. 52
Table 19 Correlation between willingness of using SPS and SPS is useful ................... 53
Table 20 Correlation between SPS is useful and SPS can reduce parking time ........... 53
Table 21 Correlation between SPS is useful and SPS can quickly payment .................. 54
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Chapter 1 Introduction
1.1 Research Background
With the increase of urban population and the development of urbanization, in many
cities of the world, looking for parking spaces become one of the problems in people'
daily life (Alicia and David, 2011). Parking problem becomes one of the major
problems of city transportation management. Arnott, Rave and Schöb, (2005) found
that approximately 30% of cars cruising in the urban area are looking for parking
spaces. Similarly, Shin and Jun (2014) said that a large number of vehicles cruise on
the road looking for parking spaces, which spent unnecessary time and generated a lot
of problems, including traffic congestion, traffic accidents, environmental issues,
energy consumption etc. Moreover, according to Caliskan, Barthels, Scheuermann
and Mauve (2007) reported that in Schwabing, Germany, every year has total two
billion Euros economic losses causing by searching for free parking spaces. To cope
with parking problems, many intelligent solutions have been developed. Smart
parking system is one of effective way. Parking guidance and information system
(PGI) is one of the early parking management systems, which using variable message
signs provide real time dynamic parking information to drivers (Teodorović and Lučić,
2006). However, with the development of information and communication technology,
advanced and efficient parking management systems have been developed.
Chinrungrueng, Sunantachaikul, and Triamlumlerd (2007) defined the smart parking
systems using effective sensors to monitor the situation of parking spaces, and then
uploading the real time data to the cloud through the large data collection tools.
Drivers can use smartphone to inquire nearby available parking spaces. This research
discussed this new type of smart parking management system. On the other hand, in
accordance with the intended use classification, smart parking system can be divided
into private and public. Private parking facilities applying smart parking systems is to
increase profits as well as improve customer satisfaction. Users in this type smart
parking system generally are customers or staff. For example, The ASDA
supermarket, in Trafford Park, employed the smart parking system to monitor the
situation of each parking spaces, which not only helping to deter non-customers over-
time occupy parking spaces, but also helping manager rational plan spaces (SMART
PARKING, 2014). Correspondingly, the public smart parking systems generally are
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construction and management by government agencies. Its purpose is to improve the
city’s public transports services. Many cities have installed public smart parking
systems, such as San Francisco, London, Beijing, in order to improve public
transportation services (Rucks and Guevara-Stone, 2013).
Although public smart parking systems are operating in some big cities as well as
there are a number of researches discussed and assessed it, a few study discuss the
perception of people towards public smart parking systems. Thus, the purpose of this
research is to investigate the citizens’ perceptions towards public smart parking
systems.
1.2 Research Aim and Objectives
The aim of this thesis is to investigate the perceptions of citizen towards public smart
parking systems in Birmingham city.
The objectives of this research presented as follow:
To investigate the people’s willingness of using smart parking systems.
To identify what benefits can be brought from smart parking systems in
people’s vision
To identify what are drawbacks of smart parking systems in people’s vision
To identify factors could hinder the development of public smart parking
systems
1.3 Methodology
In research, there are two types of methodologies commonly used, such as
quantitative (deductive) and qualitative (inductive). These two methodologies are not
mutually exclusive, but suitable to different research questions (Soifeman, 2010).
Creswell and Plano Clark (2007) described that deductive approach is top-down
method, from proposing a theory to establish hypotheses then analysis of data to
prove the theory. On the contrary, the inductance research methodology through
building broad themes to generates a theory.
The aim of this research is to investigate the people’s perceptions towards public
smart parking systems. The deductive (quantitative) methodology is suitable to this
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research question. First of all, overview relevant studies about smart cities and smart
parking systems. Through literature review, proposing a theory that citizens hold
positive perceptions of public smart parking systems. In order to capture citizens
opinions of public smart parking system, the face-to-face questionnaire will be used to
collect data in Birmingham area, in which are operating smart parking systems.
Questionnaire is an effective way to collect large number of samples as well as the
way of face-to-face has relatively high response rate than other ways (de Leeuw,
1992). The collected data are analyzed by three data analysis methods, including
descriptive analysis, analysis of variances and correlation analysis. The descriptive
analysis can help researcher detect the characteristics of samples, which possibly
influence the research conclusion (Thompson and Walker, 1998). Moreover, Botti and
Endacott (2005) stated that inferential statistics can help researchers to test the
probability of samples, which mean value represents the general condition as well as
experimental designs are used to test hypotheses about the predicted results.
1.4 Thesis Structure
In total, six chapters are discussed in this thesis. Except this chapter, the other five
chapters are organized as follows:
Chapter 2 introduces an overview of smart cities, including the evolution of smart
cities, some smart cities in the world and some key areas of smart cities. The purpose
of this chapter is to introduce research background of this study as well as leads to this
research question.
Chapter 3 detailed reviews literature concerned with the smart parking systems and
relevant studies. This chapter contains six sections, including different types of smart
parking systems, smart parking systems technologies, benefits of smart parking
systems, some smart parking system projects in the world, relevant evolution studies
on smart parking systems and the development of hypotheses. The purpose of this
chapter is to have an insight into the concept of smart parking systems and establish
research hypotheses as well as through previous studies get implications of the
research methodology of this study.
After introduction research background and overview of literature, in Chapter 4 the
detailed methodology for this research is presented. First of all, discuss the suitable
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research methodologies in this study. After discussion of methodology, the
questionnaire survey design principle and the data collect process are introduced.
Finally, the data analysis methods, such as descriptive analysis, analysis of variance
and correlation analysis are specifically presented.
Chapter 5 focuses on data analysis, which is divided into four sections. First of all, a
descriptive analysis identified collected data and got some basic findings. Analysis of
variances section discussed the effect of people’s characteristics on their opinions of
smart parking systems. Correlation analysis identified the correlation between
people’s different opinions. Finally, a comprehensive discussion of results presented
in the end of this chapter.
In Chapter 6, first of all, responding research questions and objectives of this
research and then states some limitations in this research. Finally, the
recommendation for the future work has been described.
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Chapter 2 An overview of Smart Cities
2.1 Introduction
The purpose of this chapter is to review relevant literature of smart cities as well as
introduces smart parking systems issue. The structure of this chapter is as follows:
section 2.2 introduces the evolution of smart cities. In section 2.3, some smart cities in
the world have been presented. Section 2.4 described some key areas of smart cities.
2.2 The Evolution of Smart Cities
The city is the most important product of humankind social, economic and cultural
development. World Health Organization (2014) estimates that by 2030 more than
half the world's population will live in urban areas. Bélissent (2010) stated that rapid
population growth and urbanization brought new social and economic challenges,
such as waste management, resource waste, air pollution, human health problems and
traffic congestion. In order to effectively solve problems of urban development, in
2008, IBM proposed a 'Smarter Planet' concept, which triggered a boom in global
'smart cities' development (Harrison and Donnelly, 2011). Hall (2000) believed that
smart cities would be the future direction of urban development. The concept of smart
city is being known popularly, however, the definitions of it are various in different
conditions. A more generally accepted definition is provided by Chourabi et al (2012),
defined that the smart city uses information and communication technologies and
advanced equipment to monitors and integrates conditions of all of its critical physical
infrastructures, through data collection and analysis system can better optimize its
resources, rational development and management to improve decision-making
capacity of the city, making it possible to maximize service to the public. Similarly,
Kanter and Litow (2009) supposed smart city collects data from its physical
infrastructures to improve conveniences, facilitate mobility, add efficiencies, conserve
energy, improve the quality of life, identify problems and fix them quickly, share data
to enable collaboration across entities and sectors. On the other hand, Giffinger et al
(2007) defined six characteristics of a smart city, including, smart economy, smart
people, smart governance, smart mobility, smart environment and smart living.
According to Harrison et al (2010) research, they defined the basic concepts of smart
cities are instrumented, interconnected and intelligent. These concepts extended a
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traditional concept of a physical city infrastructure to a virtual city infrastructure,
which makes a key impact on life in the city. In addition, Nam and Pardo (2011) said
creativity is considered as the engine of the development of smart cities, as well as
people, education, learning and knowledge are important aspects of smart urban
development. Meanwhile, Dirks and Keeling (2009) claimed that a city transforming
into a smart city is a journey, not a short-term activity. Cities need to be preparing for
this revolutionary change, because it will completely change the operational model of
a city.
2.3 Smart Cities in the World
Many countries around the world are developing smart cities. In San Diego, USA,
information and communication technologies (ICTs) are considered as the key factor
in the city's future development (Hollands, 2008). In Ottawa, Canada, ‘smart
community’ projects aims to create an efficient space, which through optimizing the
public networks to realize the high quality of human interaction (Wilson and Re,
2001). In the UK, Southampton City council issued multifunctional ‘Smartcities’
cards, which can be uses as bus pass, library card, or leisure card that aims to
convenience to citizens (Discover Southampton, 2014). In south-east Asia, through
launching IT2000 project, Singapore is to be transformed into an "intelligent island",
applying information technologies into society's many aspects, such as business,
education and medical, which aim to improve the quality of life of its citizens (Choo,
W, 1997). Numerous other examples abound from across the globe, Cohen (2012)
said that Vienna is under construction and development of carbon reduction,
transportation, land use planning. Barcelona successful developed solar energy project
as well as is promoting low-carbon solutions. In addition, Toronto, Paris, New York,
London and Tokyo, these big cities are in the list of development of smart city. China
shows enthusiasm towards the development of smart cities. Vine (2012) reported
Tianjin city's future development direction is to become the eco-city. Cycle routes and
tram construction throughout whole city, encouraging residents to use low-carbon
transport or walking instead of driving. The city is developing energy stations
powered combine solar energy resources to supply city. Similarly, according to the
Global Smart City (2012) reported, in order to solve the complex parking payment
process which lead to traffic problems, Wuhan, China, developed an advanced smart
payment system, parking fee informing to drivers via SMS and can use phone to
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payment. This system allows the city of Wuhan to become smartest traffic cities after
London and Singapore.
2.4 Key areas of Smart Cities
The development of smart cities involves various aspects of the city, such as smart
education, smart healthcare, smart energy, smart transportation, etc. (Nam and Pardo,
2011).
In smart education aspect, Franklin (2011) suggested that smart devices and
technologies, such smartphones, cloud technology, establish a new learning
environment for educators and it develop students’ digital literacy skills. Similarly,
Sakamura and Koshizuka (2005) said that smart mobile devices provide a condition
that people can learn anything at any place in any time, which greatly expanded the
learning space and time.
In smart medical aspect, Soller, Cabrera, Smith, and Sutton (2002) stated that smart
medical systems can improve the quality of patient treatment and life. For example,
smart medical systems use sensors, recorders and database to more effective
monitoring and recording patient information. In addition, miniaturized implantable
devices provide more effective treatment solutions. Moreover, Rocker (2011)
considered smart medical services are a great developing potential solutions that
could revolutionize the way of future health services, by providing a variety of
services to help elderly or disabled people.
In smart energy aspect, information and communication technologies provide
standardized data across various industries, which be used to control waste emissions
and plan energy consumption as well as provide innovative energy recovery
opportunities. The most important thing is to use smart and integrated method
automated energy management of systems and process (Bolla, Cucchietti and Repetto,
2012).
In smart transportation aspect, as mentioned above, by 2030, more than half of
population will live in urban areas. Transportation will be a serious problem in urban
development, leading to traffic congestion, increasing environmental pollution and
energy consumption, and adding travelling time and traffic accidents. Smart
transportation systems can be largely ease these problems and provide efficient
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transportation (Correia and Wünstel, 2011). Stefansson and Lumsden (2008) said that
as the increase of transportation volume and people's demand for traffic information,
smart transportation systems have been developed, which use information and
communication technology to strengthen the interaction between infrastructures, in
order to improve the flexibility and timeliness of information as well as increase
transport safety. Similarly, He, Zeng and Li (2010) stated that smart transportation
systems applying information and communication technology, can generate
significant social economic benefits, such as improving road capacity, decreasing
traffic accidents, saving manpower resource and reducing environmental pollution. In
addition, Idris, Leng, Tamil, Noor and Razak (2009) believed that the main reason of
traffic congestion is that too many vehicles on the road, due to the parking problem.
Smart parking system is the solution of the parking problem. Moreover, Mahmud,
Khan, Rahman, and Zafar (2013) stated that smart parking system is part of the smart
transportation systems. Due to the poor management of parking facilities led to
related parking problems. Thus, a safe, smart and efficient parking system needs to be
developed.
2.5 Summary
In this chapter, the evolutions of smart cities, smart cities in the world as well as some
key aspects of smart cities have been presented. Through reviewing these literatures,
this research topic that smart parking system has been introduced. Besides a review of
smart cities concepts, a detailed review on smart parking system and relevant
literature are discussed in the next chapter to give a deep insight of this research issue.
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Chapter 3 An overview of Smart Parking Systems
3.1 Introduction
In Chapter 2, the concepts of smart cities have been introduced. A detailed literature
review of the smart parking systems will be described in this chapter. The purpose of
this chapter is to have fully understanding of smart parking systems and then
establishes this research's hypotheses as well as through some previous studies get a
connotation of the research methodology for this research.
This chapter contains six sections. The section 3.2 introduces five types of smart
parking systems, such as parking guidance information system, transit-based
information system, smart payment system, automated parking system and e-parking.
Following section 3.2, two smart parking system technologies have been introduced
in section 3.3. Section 3.4 discuss the advantages of smart parking systems and
section 3.5 introduces some public smart parking system projects in US, CHINA and
UK. The section 3.6 presents some previous studies that contributed to discuss
people's response towards smart parking systems. Finally, this research's hypotheses
have been developed in section 3.7.
3.2 Types of Smart Parking Systems (SPS)
3.2.1Parking Guidance Information Systems (PGI)
Due to the increasingly serious of parking issues, a variety of solutions have been
developed to solve it. Parking guidance and information system (PGI) is one of the
early parking management systems. Griffith (2000, 72) said that PGI systems
provided real-time parking information to drivers and guides them to available
parking spaces, which can optimize parking process in central cities and large parking
facilities. In addition, Traffic Advisory Unit (2003) summarized some PGI systems’
benefits, such as saving parking time, saving duel and energy, reducing air pollution
and improving enforcement of parking restrictions. Moreover, Shaheen, Rodier and
Eaken (2005) found that within the last decades, a large number of PGI systems have
been implement around the world. On the other hand, Geng and Cassandras (2011)
maintained that, despite the effect of PGI systems can help drivers to find parking
spaces has been recognized, however, a small number of guide boards cannot
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guarantee the drivers accurately find parking spaces. Similarly, Waterson, Hounsellm
and Chatterjee (2001) emphasized fact that when more than one drivers go toward the
same parking spot, which will occur competition behavior. In addition, it is possible
that when drivers arrive, the parking space has been occupied, thus forcing search
again. This process wastes more time and fuel. In addition, Polak, Hilton, Axhausen
and Young (1990) reported that the effect of using PGI systems on total travel time
saving is small. The figure 1 shows parking guidance and information system variable
message signboard installed in Marina Centre area of Singapore, which can show the
number of parking lots available in car parks.
Source: Herman, R (2009). Parking Guidance System Sign Board
Figure 1 PGI system
3.2.2 Transit-Based Information Systems
Transit-based information system is another parking management system based on
PGI system model. This argument has been confirmed by Idris, Tamil, Noor, Razak
and Fong (2009), stated that transit-based information systems not only have same
functions as PGI system, but also can provide public traffic information to travellers.
Similarly, Shaheen, Rodier and Eaken (2005) said the feature of transit-based
information system can provide real-time information to motorists, including the
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number of available parking spaces in park-and-ride facilities, the departure time of
the next bus or train, and roadway traffic conditions. These information help people to
arrange time and travel plans. In addition, Chinrungrueng, Sunantachaikul and
Triamlumlerd (2007) concluded that the aim of transit-based information system is to
encourage people using public transport, increasing the utilization of public
transportation as well as reducing number of vehicle on the road, leading to ease
traffic congestion. Moreover, Orski (2003) said the transit-based information system
establish Variable Message Signs (VMS) at major roads and highways to display
these information. As shown in figure 2, a transit commuter information system
installed by Metropolitan Council, providing real time bus schedule to travelers to
help them make informed decision and improve public transit serve.
Source: Delcan Technologies (2014) Transit Commuter Information System and Real
Time Signs
Figure 2 Transit based information system
3.2.3 Smart Payment Systems
Unlike the above two parking systems, the purpose of smart payment system is to
optimize the process of paying parking fee, which is an important aspect of smart
parking systems. Shaheen, Rodier and Eaken (2005) argued smart payment systems
largely improve the efficiency of payment of parking fees, as well as parking
operators reduce operating, maintenance, and management costs. According to
Chinrungrueng, Sunantachaikul and Triamlumlerd (2007) said smart payment systems
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not only improve customer satisfaction, but also save human resources. Customers
can use a variety of payment methods, including, contactless methods (smart cards,
RFID cards), and mobile communication devices. On the other hand, Idris, Leng,
Tamil, Noor, and Razak (2009) said the highly concerned issues in the development
of smart payment systems are the privacy and security issues. This is due to the
personal information and account information needs to be highly confidential in
process of payment. Rankl and Effing (2010) stated many smart payment methods
have been developed in order to solve the limitations of traditional payment ways.
Smart cards are one of the widely used payment method in smart payment system.
Cunningham (1993) said that users only need to insert or scan their smart cards in
wireless communication devices when they entry or exit the parking garage, they can
quick complete the payment, which saves lots of time. In addition, mobile
communication devices are also increasingly used for smart payment system. The
figure 3 shows a smart meter in San Francisco’s SFpark pilot program.
Source: Jaffe, E (2014) Does San Francisco’s Smart Parking System Reduce Cruising
for a Space?
Figure 3 Smart payment system
3.2.4 Automated Parking Systems
Automatic parking system is a special system is smart parking systems which not only
use PGI system and smart payment system, but also help users parked their vehicle.
According to Rashid, Musa, Rahman, Farahana, and Farhana (2012) study, they
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argued that automatic parking system greatly reduces the operation of people on the
parking process. Similarly, An, Choi and Kwak (2011) considered that automatic
parking system cannot only reduce the cost of parking vehicles, but also reduced air
pollution, energy consumption and parking time. On the other hand, Hanche, Munot,
Bagal, Sonawance and Pise (2013) developed an automatic parking system based on
RFID technology, which installing RFID readers, tags and barriers at the entrance and
exit of the car park to automatically detect parking traffic flows. This technology not
only can be implemented the parking and payment automation, but also reduce labor
resources. In addition, Jeevan, Harchut, Bessler and Huhnke (2010) said automatic
parking system provides both convenience and safety benefits to drivers. On the other
hand, Shaheen, Rodier and Eaken (2005) considered automated parking system
should be built in the city central area, where have higher property values and parking
demand, which can meet the usage amount and operational costs of the automated
parking system. The figure 4 shows an automated parking system in Birmingham,
England.
Source: Skelley, J (2012). Why America Needs More Robotic Parking.
Figure 4 Automated parking system
3.2.5 E-Parking
As development of technologies, the function of smart parking system has also been
upgraded. The innovative smart parking system is E-parking. Hodel and Cong (2003)
defined the concept of E-parking is that drivers use cellphone, PDA or Internet
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inquiry available parking spaces as well as reserve a parking space as well as can use
credit card or phone pay for parking fees. They think this parking space optimization
service acts as a parking brokerage service. Similarly, Shaheen, Rodier and Eaken
(2005) said E-parking could potentially provide a cost-effective method to optimize
existing parking resources. Geng and Cassandras (2011) supposed the new smart
parking system can base on driver's requirements that combine proximity to
destination and parking cost, distribute and reserve parking spaces to the driver. Such
a smart parking system not only ensure the capacity of the entire parking lot have
been fully utilized, but also greatly reduces the problems posed by drivers of
individual behavior. New smart parking systems significantly improved PGI systems.
In addition, Hanif, Badiozaman, and Daud (2010) found a new smart parking system
that drivers use short message service to reserve their parking spots, ensuring there
has available a parking space when they arrived the destination. A more
comprehensive definition of smart parking system is provided by Chinrungrueng,
Sunantachaikul and Triamlumlerd (2007) said smart parking systems applying
advanced technologies effectively use parking resources and combined with transit-
based information reasonable adjust parking fee. In addition, smart parking system
can also provide navigation, reservation and parking fee payment functions to better
meet the diverse needs of users. The figure 5 shows an on-street in-ground sensor,
which is used to detect the occupation status of parking spaces in smart parking
systems.
Source: SMART PARKING (2014). SmartPark- turn your city in to a smart city.
Figure 5 Smart parking system
15
3.3 Smart Parking System Technologies
3.3.1 RFID
In recent years, radio frequency identification (RFID) technology has received wide
recognition and has been applied to many fields. Its feature is that rapidly identify
many tags in the same area without human assistance (Want, 2006). The strength of
RFID technology has been implemented into solves parking issues. Hanche, Munot,
Bagal, Sonawance and Pise (2013) said RFID technology could be used to maximize
the use of parking resources. For example, Rivenburgh and Slemmer (2006)
developed an automated RFID parking management system, employing RFID tags
built-in the vehicle and RFID readers install in the gate to detect the vehicle. The
collected data will send to the database. This system can enhance the utilization of
parking space and help user check the availability of the parking space. Similarly,
Ganesan and Vignesh (2007) developed a parking space allocation system based on
RFID technology, containing an appropriate RFID tag and two RFID readers. This
system provides particular available parking space location information to users by
short message service (SMS). In addition, Ostojic, Lazarevic and ovanovic (2007)
said that parking space occupation data be collected by RFID system, and these
information can update in real time via the Internet. Users can reserve their parking
spaces by phone. Moreover, PALA and Inanc (2007) believe that RFID technology is
a very important application in the smart parking system. It can optimize the parking
process lead to alleviate traffic problems. Vehicles check-ins and check-outs will be
quick and automated operation in the parking lot, rather than driver have to stop car to
deal with, which accelerated parking efficiency as well as reduce the cost of human
resource. Jian, Yang and Lee (2008) stated RFID technology can also be applied to
vehicles tracking system and claim the modern and convenient parking system based
on RFID technology will be developed.
3.3.2 Wireless Sensor Networks
Wireless sensor networks (WSN) are applied to smart cities domains, such as smart
home, intelligent buildings, health-care and others (Lynch and Loh, 2006). WSN low
power consumption and low cost advantage is widely used to the smart parking
systems, which is used to detect parking information and provided these information
is used to calculate the shortest path (Idris, Tamil, Noor, Razak and Fong, 2009). Lee,
16
Yoon and Ghosh (2008) said wireless sensor networks can provide economic and
practical solutions for smart parking systems. Tang, Zheng and Cao (2006) believe
that wireless sensor networks as a very promising technology will be used in future
smart parking systems. Chinrungrueng, Sunantachaikul and Triamlumlerd (2007) said
smart parking system based on wireless sensor networks are easier to install and
maintain and has better accuracy than traditional loop detector. Similarly, Srikanth,
et al, (2009) declared that WSN technology could be the key to solving the growing
parking problem. Wireless sensor networks have simple installation, maintenance and
relatively inexpensive benefits, which suitable to rapid transformation of existing
parking facilities.
3.4 The Benefits of Smart Parking Systems
According to using purpose, parking facilities can be divided two types, such as
private and public. For private parking facilities, smart parking systems can better
utilize parking resources in order to increasing operators' profits (Shaheen, Rodier and
Eaken 2005). For example, ASDA located in Manchester City at Old Trafford, the
smart parking system significantly reduces the number of non-customer vehicles
parked in ASDA car park beyond maximum stay time, which ensuring provide the
available car parking to potential customer. In addition, the system reports the car
park usage is only about 70%. As a result, ASDA management decided to extension
those unused spaces to the shop (SMART PARKING, 2014). However, the public
smart parking systems can bring many aspects benefits. In environmental terms, smart
parking systems effectively reduce average parking time as well as facilitate payment
process, reducing vehicle exhaust emissions and fuel consumption. Thus, smart
parking systems play a role in easing air pollution as well as saving energy (Idris,
Leng, Tamil, Noor, and Razak, 2009). Smart parking system increase users'
satisfaction through providing real-time available parking space information to drivers,
making vehicle travel time more efficient and reduce the time to search a parking
space. In government aspect, smart parking systems collect parking spaces usage data
and upload the information to a central database. Department of transportation
according the traffic condition adjust parking fee in order to mitigation of traffic
congestion (Clapper, 2000). In addition, due to the smart parking systems can
monitor condition of parking spaces, the number of illegally parked vehicles
significantly reduced improving traffic efficiency (Kurogo, Takada, Akiyama, 1995).
17
On the other hand, Mirani (2014) said smart parking systems can bring the many
benefits, in addition to reducing traffic congestion, energy consumption and vehicle
emissions, it can promote local economic development. For example, people have
more time to shop, eat and entertainment rather than looking for parking spaces.
3.5 Public Smart Parking System Programs in the World
3.5.1 Smart Parking Systems in US
Smart parking systems have been serving for public in the United States. Markoff,
(2008) reported that San Francisco developed based on wireless sensor networks
smart parking system to solve difficult to find parking spaces problem. Driver can use
smartphone to find a parking space rather than simply rely on maps or roadside signs
and people can pay for parking by phone. Shaheen, Rodier and Eaken (2005) stated
that parking PGI systems have been developed in New York City, the system can
display real-time information about parking availability via dynamic message signs,
drivers can use these information quickly find the parking spaces. Similarly, Orski
(2003) said Baltimore-Washington International Airport implemented the most
advanced PGI system, which uses ultrasonic sensors to monitor parking spaces and
uses lighted electronic signs guidance drivers to available parking spaces and indicate
each parking space occupancy status. Moreover, in Chicago, a smart parking system
has been developed, which can collect real-time traffic information, including location
of available parking spaces near the large parking lot or garage, train or bus departure
time and traffic condition, sent to tourists by VMS. (Shaheen, Rodier and Eaken,
2005). A number of cities in the U.S. use debit cards with smart electronic parking
meters. For example, the city of Berkeley, California, using smart cards with smart
meters to pay the parking fee. Moreover, the City of Fort Lauderdale is implementing
technology from Streetline aimed at making it easier to find parking in downtown
(STREENLINE, 2012).
3.5.2 Smart Parking Systems in China
China is also rapidly developing smart parking systems in many big cities. For
instance, Beijing Olympic Park implemented a parking guidance information system
in eight public parking lots. This parking guidance system consists of four basic
subsystems: data acquisition systems, data communications systems, information
18
release system and central control system. Vehicle detectors installing each park gate
entrances and exports as well as car park lots to monitor the direction of vehicle,
through the counter calculate the number of vehicles detected by detectors. This
information upload to local central computer and the available parking space number
and location will be displayed in each parking lots directional board. In addition to
parking guidance system, the entire car park management system also includes image
recording system and smart parking payment system, which form a fast, efficient
parking management system (Shenzhen Fushi smart system Ltd., 2011). Wuhan,
China, implemented new smart parking system cover 12 commercial districts, drivers
can use smartphone query parking spaces, reserve a parking space and navigation to
parking space. The smart parking management systems release real-time parking
information to the public via ETC electronic tags, cell phones, guidance boards, radio
and other means (Changjiang daily, 2013).
3.5.3 Smart Parking Systems in UK
United Kingdom is one of the earlier country developing smart parking systems. For
example, as early as 1991, Leeds, England, implemented a parking guidance
information system, which includes ten Variable message signs located along a
circular route around the city center containing 6 car parking lots. Each VMS displays
at least two-car parking lots available status, such as SPACE, ALMOST FULL, or
FULL (Thompson and Bonsall, 1997). Similarly, Smith and Roth (2003) said that
parking guidance information systems have been applied to several parking facilities
providing location and guidance services to drivers in Bristol, England. This system
can also base on the daily statistics to predict the parking situation in next a few days.
For the new smart parking system, SMART PARKING (2014) declared “SmartPark is
already operating in cities like Birmingham, and in the central London Borough of
Westminster". Mirani (2014) reported that the city of Westminster, one of London's
local councils, would begin installing the first batch of 3,000 sensors to the street.
This project aims to help drivers quickly find parking spaces, making London become
the world's first smart parking revolutionary city.
19
3.6 Related Studies
3.6.1 PGI system
Thompson and Bonsall (1997) did a research about this topic, which compared and
summarized some related driver's response of PGI system studies in different places
and different studies, and then got some conclusions. It uses a secondary research
methodology. Their study is very helpful to this research that investigate people’s
perceptions of new smart parking systems. They mentioned a lot of studies used
questionnaires to collect data, but it has its limited to collect driver's response
information. The drivers' response can be divided into awareness and usage rate two
categories, which can be gather from different groups of drivers, such as, age, gender,
trip purpose and frequency. On the other hand, most of studies only use descriptive
analysis; some more powerful statistical techniques should be used in analyzing the
drivers' parking behavior. In addition, they claim that PGI systems need to be
designed from users’ angle and the users' response to PGI systems is strongly related
to drivers' level of knowledge and the ability to understand and receive information.
3.6.2 Transit-Based Information System
Rodier and Shaheen (2010) described the result of initial focus groups and survey of
participants in a transit-based smart parking field operational test, which collected
three types information, such as demographic attributes, commute needs and
constraints as well as commute travel behavior. In addition, Some descriptive analysis
have been done in their work, which can be used to analysis the transit-based smart
parking system whether has an impact on the participants’ behavior, as well as
whether the system is increased transit ridership. Through data analysis, they believe
that transit-based smart parking system has been recognized by participants, and in
some extent increased public transport usage.
3.6.3 Smart Payment System
Xu (2007) did a research about the smart card applications, which aimed to identify
the public perception of using smart card payment comparing with conventional
payment ways, as well as discussed the user's demand and benefits and effectiveness
of smart card ticketing. In his research, the revealed preference and stated preference
surveys are used to collect the data, which be analyzed by standard logit models. In
20
addition, fuzzy logic (FL) and artificial neural network (ANN) methods are used to
model discrete choice data. He claim that smart card payment ways have benefits to
public transports users, such as providing multifunction, having geographic areas
covered and top-up options. On the other hand, smart card payments are also help
relevant policy making to enhance the service quality of public transport systems.
3.7 Hypotheses development
3.7.1 The Impact of Personal Characteristic on Parking Issues
People’s parking behaviors or responses are influenced by personal characteristics,
such as gender, age, education, trip frequency, etc. (Van der Waerden, Timmermans,
and Silva, 2014). Similarly, Griffioen-Young et al. (2004) stated that personal
characteristic could directly indicate parking behavior. Rodrıguez and Joo (2004) said
that individual factors, such as gender and age, would help determine difference in
systematic utilities across different conditions. Bao, Deng and Gu (2010) did a
research about the impact of parking charge on residents' travel mode and traffic
condition. In their research, the investigation contents include traveler’s gender, age,
income, traffic mode, parking fees, parking time and other variables. Based on above
literatures, this study propose the following main hypotheses and sub-hypotheses:
H1: People’s characteristics affect their opinion that without using smart parking
system is difficult to find a parking space.
H1a: Age has an effect on people's opinion that without using smart parking
system is difficult to find a parking space.
H1b: Gender has an effect on people's opinion that without using smart
parking system is difficult to find a parking space.
H1c: Income has an effect on people's opinion that without using smart
parking system is difficult to find a parking space.
H1d: Driving age has an effect on people's opinion that without using smart
parking system is difficult to find a parking space.
H1e: Average parking time has an effect people's opinion that without using
smart parking system is difficult to find a parking space.
H2: People’s characteristics affect their willingness of using smart parking systems.
H2a: Age has an effect on people's willingness of using smart parking systems.
21
H2b: Gender has an effect on people's willingness of using smart parking
systems.
H2c: Income has an effect on people's willingness of using smart parking
systems.
H2d: Driving age has effect on people's willingness of using smart parking the
systems.
H2e: Average parking time has an effect on people's willingness of using
smart parking systems.
3.7.2 Potential Correlations between Parking Behaviors
People's parking behaviors possible exist potential correlations (Taylor, 1990). In
addition, Chen and Lai (2011) presented that some approaches can be used to
determine people's behaviors, such as providing respondent different choice situations
and asking them to evaluation these situations. Moreover, Bao, Deng and Gu (2010)
said through comparing two behaviors can get a deeper insight of habitual parking
behavior. According to Van der Waerden, Timmermans, and Silva (2014) study, who
use multinomial regression analysis to investigate the relationship between the
respondents’ habitual parking behaviors and their personal and trip characteristics.
Thus, there proposed more hypotheses as following:
H3: People consider without using smart parking system to find a parking space is
difficult things has correlation with people’s willingness of using smart parking
system.
H4: People are willing to use smart parking system has correlation with people think
smart parking system is useful.
H5: People think smart parking system is useful has correlation with people think
smart parking system can reduce parking time.
H6: People think smart parking system is useful has correlation with people think
smart parking system can quickly pay parking fee.
The initially proposed research model is presented in figure 6. This model illustrates
the people's perceptions towards smart parking systems construct as well as shows the
relationships among 'difficult parking', 'willingness of using smart parking system',
'consider smart parking system is useful', 'smart parking system can reduce parking
time', and 'smart parking system can quickly pay parking fee'. In addition, this figure
also presents the connection of five personal characteristics with opinions of ‘difficult
parking’ and ‘willingness of using smart parking system’.
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Figure 6 Proposed research hypotheses model
3.8 Summary
In this chapter, first of all, through describing the different types of smart parking
systems, researcher has a primary understanding of the smart parking system of this
research. Secondly, from the introduction of two major technologies used in smart
parking systems, researcher generally realizes its work principle. Next, some benefits
discussed from the private and public parking aspects, which can be brought from the
smart parking systems. Some public smart parking system projects in the United
States, China and the United Kingdom has been introduced. In addition, this chapter
analyzed and summarized three studies about smart parking system evaluation that
have reference value for this study. Finally, based on the literatures of smart parking
system and studies of people' parking behavior, the researchers established hypotheses
of this study.
Therefore, based on the smart parking system related literature review as well as the
establishment of research hypotheses in this chapter, research methodology of this
research is generated and discussed in details in the next chapter.
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Chapter 4 Research Methodology
4.1 Introduction
Through reviewing the concept of smart cities in chapter 2 as well as relevant studies
of smart parking system in chapter 3, this chapter discusses the detailed research
methodology.
First of all, the appropriate research methodologies, inductive and deductive approach
as well as qualitative and quantitative research methodology, have been discussed in
section 4.2. Section 4.3 introduced the process of survey design. Following by section
4.3, the data collection process is introduced in section 4.4. The data analysis stage in
section 4.5, which contains three parts: descriptive analysis, analysis of variance and
correlational analysis.
4.2 Discussion of Research Methodologies
4.2.1 Inductive Versus Deductive Research Method
The decision of research methodology is important issue in a research. Saunders,
Lewis and Thornhill (2011) pointed out that researcher needs to make a choice
between two major research methodologies, such as deductive and inductive.
Deductive research is based on existing knowledge and research establishes
hypotheses to deductive and tests the theory (Cormack, 1991). Goddard and Melville
(2004) defined that inductive research is based on observations and through data
analysis to develop a theory. Blaxter, Hughes and Tight (2010) noted that the
inductive approach gives researchers the chance to understand the purpose of research
and study knowledge, allows researchers to reveal the other side of the issue. Gray
(2004) indicated that inductive approach is usually combined with qualitative method
to collect data and find different aspects of the problem. Similarly, the deductive
approach often associated with quantitative method, using statistical methods usually
begins with a theory or hypothesis, through collecting data and applying descriptive
or inferential statistical methods to validate theory or hypothesis (Rajasekar,
Philominathan and Chinnathambi, 2006). The researcher decided to adopt deductive
approach because the idea of this research initiated by the research of evaluation of
smart parking systems. Researcher establishes a new theroy based on previous
24
relevant studies, through discussing findings to reject or confirm the effect of personal
characteristics on opinions of smart parking system as well as the correlation between
different parking behaviors. The limited time is another reason to apply the deductive
approach. Bryman and Bell, (2007) stated inductive study require long time
observation and data collection in order to develop a theory, however, deductive
approach can through analysis data to investigate a specific theory or hypothesis.
4.2.2 Qualitative Versus Quantitative Research Method
Most researches based on either qualitative or quantitative methodologies (Bryman
and Bell, 2011). Silverman (2013) said the decision to choose a specific methodology
should be based on its suitability to research questions and types of information need
be collected and analyzed. According to Hennink, Hutter, Bailey (2011) study, the
characteristic of qualitative research is using complex textual descriptions to explain
people's opinion of a specific research project. Conversely, Rajasekar, Philominathan
and Chinnathambi (2006) described quantitative research is based on measurement of
the number or amount to understand research questions. Patton and Cochran (2002)
stated qualitative research generally aims to understand people's experiences and
attitudes of things, relating to 'what', 'how to' or 'why' phenomenon rather than 'how
many' or 'how much ', which are answered by the quantitative research.
On the other hand, Tewksbury (2009) stated that the difference between qualitative
and quantitative research method contains some major aspects, including: analytical
objectives, types of research questions, types of data collection methods used, types of
data produced. Kumar (2011) said that qualitative research has three common data
collection methods, such as participant observation, in-depth interviews and focus
groups. Each method is particularly suited for gathering specific typed of data.
Correspondingly, questionnaire is most common data collection method in
quantitative research. Oppenheim (1992) described that questionnaire is a potentially
cost-effective research method, which can collect large amounts of data. Denscombe
(2010) said the important advantage of questionnaire is that it provides standardized
answers, which not only facilitate the respondents' answers, but also conducive to the
analytical work of researchers. Comparing with questionnaire, the advantage of
interviews is that interviewers can explain questions if respondents cannot understand
as well as can gather more information. However, interviews way is very time-
consuming activity particularly in transcript process (Burcu, 2000). On the other hand,
25
Phellas, Bloch and Seale (2011) stated the form of focus groups is very similar to
interviews, but its participants should not more than seven members. So, interviews
and focus groups methods are suitable to analysis small-scale population research
activity.
Moreover, Sofaer (2002) stated the analysis of qualitative data is the most challenging
part for this method. Qualitative data cannot directly used quantitate analysis models
and tools. In addition, qualitative analysis of the data is essentially suggestive, rather
than conclusive. Similarly, Mack, Woodsong, MacQueen, Guest and Namey (2005)
found that in quantitative research, the findings and conclusion could be separately
presented, however, qualitative research need to distinguish between observations and
interpretations of those observations. However, Abeyasekera (2005) supposed data
analysis methods of quantitative research could help researchers get meaningful
results from a large number of data. The most important is that it can exclude a large
number of confounding factors, which tends to affect the research conclusions.
The purpose of this research is to investigate the people's perception of smart parking
system, which need statistics large number of data. Accordingly, this study tends to
use quantitative research methodology. First, establishing a theory that people hold a
positive attitude on smart parking system. Then, using questionnaire survey from
different aspects collects numerical data on people's perception of smart parking
systems. Finally, using quantitative analysis methods and tools to verify hypotheses
and prove the theory.
4.3 Survey Design
This research is a quantitative study, using the questionnaire to gather people'
opinions. In total, the questionnaire contains nineteen questions, involving, single
choice questions, multiple-choice questions, rating and an open-ended question. The
collected data types consist of nominal data and ordinal data. The Likert scales are
used in this questionnaire.
The structure of people's perception of smart parking system questionnaire is that: in
the section 1, some questions about user's personal information, such as gender, age,
incomes and driving age. Following the section 1, some conditional questions about
smart parking system are included in the section 2. (Please refer to Appendix C). In
26
the section 2, the following variables are consider presenting relevant questions to
respondents:
Parking experience: reveals respondents' the attitude of the parking problem and
parking experience.
Attitudes of smart parking system: contains whether or not used smart parking
system and the satisfaction of it, the willingness of using smart parking system, and
whether think smart parking system is useful, which contains it can reduce parking
time and quickly payment two aspects.
Assess smartphone application: the opinion of using smartphone as tools and the
demands of functions.
Assess smart parking system: rating the benefits and drawbacks of smart parking
system. In addition, whether approve the development of smart parking system and
the opinion of factors that hinder development.
Suggestions: expect and suggest to smart parking system.
As to detailed question of the survey, please refer the questionnaire in Appendix C. In
the questionnaire, questions 1-4 in section 1 are respondents' personal information,
Parking experience is in questions 5 and 6; Attitudes of smart parking system is in
questions 7, 8, 9, 10, 11, 12; Assess smartphone application is in questions 13, 14;
Assess smart parking system is in questions 15, 16, 17, 18. Suggestions are in
question 19.
4.4 Data Collection
The population of the data collection is defined as people live in Birmingham area,
England, who has qualification and experience of driving vehicle. The main reason to
select Birmingham as the survey location is that public smart parking systems, using
smartphone to inquire and navigate to parking spaces, are widely installed in this city.
In addition, the Birmingham is closer to Sheffield. Hence, the survey costs can be
minimized. In order to collect as much as possible valid data, the survey is conducted
in car park spots, city library and shopping mall, etc. densely populated public areas
in urban area.
27
On the other hand, questionnaire survey response rate tends to be unpredictable and
there may be very low, making it impossible to obtain sufficient data (Cameron and
Price, 2009). Thus, this study uses face-to-face way. Mason (2014) said face-to-face
survey has a relatively high response rate as well as better quality of the data than
other type questionnaire surveys. However, the biggest drawback of this type of
survey is time-consuming. There are several reasons of using face-to-face way. First,
the place of data collected is in Birmingham, researcher neither has postal address nor
email ID. Secondly, smart parking system is a new topic, face-to-face way provides
an opportunity that researcher can explain the features of new smart parking system as
well as get more information when communicate with respondents.
The survey lasted for 5 days from 31 July to 4 August in 2014. Using face-to-face
questionnaire to collect data, most respondents accept the survey request. In total, in
this data collection activity collected 150 questionnaires. After screening and
verification, remaining 105 valid questionnaires can be used to do statistics analysis.
4.5 Data Analysis Methods
4.5.1 Descriptive Analysis
Generally, the first step in statistical analysis is to identify collected data, which need
to do a descriptive analysis using numerical and graphical methods to integrate and
summarize collected data (Thompson, 2009). Descriptive statistics help researchers
with a reasonable way to simplify a lot of data (Jaggi, 2003). Descriptive analysis
includes several methods, including, distribution, central tendency and dispersion.
Firstly, distribution method lists participants' basic information, such as, gender, age
and income, driving age, etc. The most common ways of distribution are frequency
and percentages in a table or a graph, which are more meaningful than number
description. Graphs make it easier to find certain characteristics and trends in a set of
data. Distribution can be used to analysis both quantitative and qualitative data (Jaggi,
2003). For instance, using histogram indicates the number of participants of different
ages as well as the number of participants of different average parking time. Using pie
chart shows the proportion of male and female participants.
Secondly, measuring of central tendency is used to identify the characteristics of a
group of data. This method helps researchers obtain basic attribute of a particular set
28
of data. The common measures are the mean, the mode and the median (Botti and
Endacott, 2005). For example, measuring mean of rating data of people's willingness
to using smart parking system, which can help researcher identify the basic opinion of
this issue.
However, a measure of central tendency alone is not sufficient to describe a frequency
distribution. In addition to it there are need use dispersion methods. Standard
deviation is the most common computing discrete method for descriptive analysis
(Fisher and Marshall, 2009). Standard deviation calculates the value of each data vary
from mean, when all the data are same, the standard deviation is 0 (Taylor-Powell,
2003). It can be used to detect the 'special' data affect extent to results. In other words,
standard deviation is used to describe the volatility of the data. For example, male and
female have the same mean value of considering smart parking system is helpful, but
the standard deviation indicate the degree of difference in opinion.
4.5.2 Analysis of Variance
Analysis of Variance (ANOVA) is a hypothesis-testing technique used to test the
equality of two or more samples means by examining the variances of samples that
are taken (Bolton, 1997). Due to various factors, data obtained from research are
fluctuated, which possible caused by two categories, ANOVA can determine whether
the differences between the samples are caused by random error (sampling errors) or
whether due to systematic treatment effects that causes the mean in one group to
differ from the mean in another.
Random errors: Differences due to measurement error or differences between
individuals, which called the within group differences, denoted as SSw, the degrees of
freedom (dfw).
Systematic treatment: Difference caused by the different processing, referred to as
between group differences, denoted as SSb, the degrees of freedom (dfb).
The total sum of squared deviations: SSt= SSb+SSw
Within group SSw and between group SSb respectively divided the degrees of
freedom (within group dfw = n-m, between groups dfb = m-1, where n is the number
of samples, m is the number of groups), obtained mean square MSw and MSb. One
29
situation is the processing no worked, means that each group of samples are from the
same population, MSb / MSw≈1. Another is the processing worked, so mean square
error caused by error and different processing, meaning each samples come from
different population. So, MSb >> MSw. The ratio of MSb / MSw constitute F
distribution. Comparing F value with the critical value and deduce whether each
samples come from same population (Miller and Haden, 1988).
This study uses one-way ANOVA that used to study whether one control variable in
different levels had a significant impact on observed variables (Sedgwick, 2012). For
example, analyzing the effect of age variable on people's willingness of using smart
parking systems. People's age is control variable, which contains four different age
groups, such as 17-29 years old, 30-45 years old, 45-60 years old and over 60 years
old. The observed variable is the mean value of people's willingness of using smart
parking system.
On the other hand, the analysis of variance (F-test) is based on overall variance
homogeneity within each experimental group. Thus, before doing analysis of variance,
it should first test homogeneity of variances within overall experimental group. This
test aimed to test the null hypothesis that the variances of the groups are the same. If
the Levene’s test is significant than the P-value is less than 0.05, then the variances
are significantly different for this variable. As a result, this variable did not need to do
analysis of variance. Because, its F-test result possible attributed to difference of
variances within overall each experimental group.
The basic process of one-way ANOVA:
1. Test the homogeneity of variances
2. Establish the testing hypotheses (e.g. two groups in one variable)
Null hypothesis: H0: μ1
= μ2
Alternative hypothesis: H1: μ1
≠ μ2
Test level = 0.05
3. Calculate F value
4. Determine the P value and make inference results
30
4.5.3 Correlational Analysis
Correlation analysis is another common data analysis methods of quantitative research,
which mainly used for ordinal data analysis including Likert scales or other rating
scales (Choi, Peters, and Mueller 2010). There are some correlation analyses methods
can be used to analyze the ordinal data, such as Spearman’s correlation coefficient,
Kendall’s tau and Polychoric correlations (Kendall and Gibbons 1990). In this study,
the Spearman's correlation coefficient will be used to analysis the correlation between
variables. Spearman correlation coefficient is a statistical measure of the strength of a
monotonic relationship between paired data. It is often used as a statistical method to
prove or deny a hypothesis (Kendall and Gibbons 1990). For example, Whether or not
people consider without using smart parking system to find a parking space is difficult
thing correlate to their willingness of using smart parking system.
A commonly used formula of measuring correlation is represented as follow, which
Spearman’s correlation coefficient is denoted by 𝑟𝑠.
𝑟𝑠 =∑ (𝑋𝑖 − 𝑋)(𝑌𝑖 − 𝑌)𝑁
𝑖=1
√∑ (𝑋𝑖 − 𝑋)2𝑁𝑖=1
√∑ (𝑌𝑖 − 𝑌)2𝑁𝑖=1
𝑟𝑠 is a vector. The magnitude of 𝑟𝑠 represents the degree of correlation between two
variables. The closer 𝑟𝑠 is to ±1 the stronger the monotonic relationship. The sign of
𝑟𝑠 represents the variation trend of the correlation between the two variables. ‘+’
represents a positive correlation between the two variables, on the contrary, ‘-
’indicates a negative correlation between the two variables. So it can verbally describe
the strength of the correlation using the following guide for the absolute value of 𝑟𝑠:
.00-.19 “very weak”
.20-.39 “weak”
.40-.59 “middle”
.60-.79 “strong”
.80-1.0 “very strong”
In addition, Fisher and Marshall (2009) stated that the ways of measurement is based
on the data level of variable, such as nominal, ordinal, and continuous (Interval/Ratio).
The calculation of Spearman’s correlation coefficient and subsequent significance
31
testing of it requires the data are ordinal level and above as well as monotonically
related.
4.6 Summary
This chapter firstly discussed the appropriate methodologies in this research and got a
conclusion that this research suitable for use deductive and quantitative research
methodologies. Researcher proposed a hypothesis that people hold a positive attitude
to smart parking system, which need use questionnaire to gather data and apply data
analysis methods to reject or verify this theory. The detailed questionnaire survey
design and implement processes have been described in this chapter. In addition, three
main quantitative data analytical methods have been specifically introduced in the end
of this chapter.
In the following chapter, detailed data analysis processes and discussions of results
will be presented.
32
Chapter 5 Findings and Discussions
5. 1 Introduction
In the chapter 4, the data collection method and process as well as the data analysis
methods for this research have been detailed described. This chapter concentrated on
analyzing and discussing collected data. The purpose of this chapter is to have an
insight into people’ perceptions of smart parking system, measuring by descriptive
analysis, one-way ANOVA and correlation analysis methods. The structure of this
chapter is introduced as follow. In this research, the Microsoft Excel and IBM
SPSSatistics version 22 tools are used to statistic and analysis collected data. First of
all, section 5.2 presents the descriptive analysis part, including, basic characteristic of
respondents, people's attitudes towards smart parking system and assessments of
smart parking systems advantages and disadvantages. Following the descriptive
analysis, One-way analysis of variance is described in section 5.3. The section 5.4
discusses correlation analysis of four paired variables. In section 5.5, a comprehensive
discussion is conducted as well as summarizes research hypotheses.
5.2 Descriptive Analysis
5.2.1 Characteristics of Respondents
In this section, participators’ basic information have been demonstrated via bar charts,
pie charts, which can help researcher to understands the actual situation in this survey,
including gender, age, income, driving age, average parking time and whether used
smart parking systems and the satisfaction.
Gender
The proportion of gender in this survey can be reflected the reality of the society,
(figure 7), composed of 45% female and 55% male overall, which basically conform
to the official percentage of sex in UK showed male/female=0.96 (Office for National
statistics, 2012). A possible reason of the percentage of female in this survey slightly
less than male is that the total number of male who hold the license and experience of
driving is more than female in UK. According to UK government statistic estimated
in 2013, 16.9 million male drivers and 14.9 million female drivers (Department for
Transport, 2014).
33
Figure 7 Gender proportion
Age
As can be seen in figure 8 for the proportion of age. There have four age groups,
including 17-29 years old, 30-45 years old, 45-60 years old and 60 years old+.
Distribution of the respondents’ age concentrated in the first two age intervals, age
between 17-29 years old occupied 42% and 37% respondents’ age was between 30-45
years old, however, the posterior two age groups only had 11% and 10% respectively.
There have some reasons can explain to such age distribution. Firstly, the survey
usually conducted at the densely population areas during peak time, it would be
normal that the number of elder people was far less that the younger population. In
addition, according to the Office for National statistics (2012), the percentage of
people over 65 years old was 16%, 65.8% people were 15-64 years old in 2011.
Therefore, based above reasons, only 10% of respondents’ age over 60 years old is
still explainable and acceptable.
Figure 8 Age proportion
55%
45%
Gender
male
female
42%
37%
11%
10%
Age
17-29
30-45
46-60
60+
34
Income
The respondents' income distribution is represented in the figure 9. In this survey, 58
respondents’ income is less than 30,000 pounds and 25 respondents’ income is at the
level of 30,000-50,000 pounds as well as 19 respondents and 3 respondents' income is
at 50,000-80,000 pounds and higher than 80,000 pounds respectively. This income
distribution and percentage in this survey meets the facts. According to The World
Bank statistic (2014), in 2013, the UK GDP per capita about 39,351 USD
(23,571GBP).
Figure 9 Income distribution
Driving age
Driving age is an important factor that could be affects the participators view of using
smart parking system. As can be seen in figure 10, about half of participators' driving
age over 10 years. The driving age less 1 years and driving age 6-10 years have the
same percentage (19%). The percentage of participators' driving age between 1-5
years (13%) is slightly less than these two groups. In overall, about 70% of
participators have more than 5 years driving experience. A large number of
respondents have rich driving experience, and their views will be explainable and
valuable. On the other hand, the remaining respondents' opinions represented the view
of drivers who lack driving experience, which also has analytical value.
58
2519
30
10
20
30
40
50
60
70
less 30k 30k-50k 50k-80k 80k+
Income
people
35
Figure 10 Driving age proportion
Average parking time
Figure 11 showed four groups of participators' average parking time. As can be seen
that the number of participator who spend less than five minutes or more than 20
minutes to find a parking space is little numbers (9 persons and 5 persons
respectively). However, spending 6-10 minutes to find a parking space group has a
large number of participators (55 persons), as well as 39 participants' average parking
time is between 11-20 minutes. According to Westminster city council stated drivers
need 15 minutes to find a parking space in this area (BBC NEWS, 2014). Therefore,
the samples in this survey basically reflected the reality of the society.
Figure 11 Average parking time distribution
19%
13%
19%
49%
Driving age
less1year 1-5 years 6-10 years 10years+
9
55
36
50
10
20
30
40
50
60
less 5 minutes 6-10 mintues 11-20 mintes 20mintues+
Average parking time
people
36
Usage rate of smart parking systems
According to figure 12, only 18% of participators claimed they used smart parking
system before. There are some reasons that can explain low usage rate of smart
parking system. Firstly, although the SMARTPARKING (2014) announced that their
Smart Parking program has been operating in the Birmingham area, through
communication with respondents during face-to-face questionnaire survey, researcher
found that the majority of participators did not know Birmingham has implemented
such a smart parking system. Secondly, according to eMarketer (2014) expected
mobile phone users will reach 4.45 billion in 2014, of which 48.9% people will use
the mobile Internet at least monthly. Smartphone users reached 1.75 billion in 2014.
Smart parking systems required drivers use smartphone query real-time parking
information via 3G networks. The proportion of mobile Internet users and the number
of smartphone users may affect the proportion of the use of smart parking system.
Figure 12 Usage rate of smart parking systems
Satisfaction of smart parking systems
Figure 13 showed the satisfaction of smart parking systems from participators who
used it before. Although the usage rate of smart parking systems in this survey is
lower than expected, from evaluations of those participators, people's satisfaction of
smart parking systems is quite high. Indeed, public smart parking systems are
operating by SMART PARKING Ltd in the Birmingham area, which covered a large
area, which can provide nearest parking spaces information to drivers, including
location, parking rates, phone number, picture, as well as can provide navigation
service that is compatible with the phone's map navigation function. (Actual function
pictures represented in Appendix B).
YES
18%
NO
82%
Used Smart parking system
YES NO
37
Figure 13 User satisfaction of smart parking systems
5.2.2 People's attitudes towards Smart Parking Systems
This section presented participators’ opinions of smart parking systems. Likert scales
are used to represent the level of opinions. For example, ‘1’: Strongly Disagree, ‘2’:
Disagree, ‘3’: Neutral, ‘4’: Agree and ‘5’: Strongly Agree. Through comparing mean
value of smart parking system's different issues got a preliminary insight of
participators' perceptions. Table 1 showed some participators’ opinion related to smart
parking systems.
Table 1 People’s opinions of smart parking systems
N Mean Std. Deviation
Difficulty Parking 105 3.9333 .81177
Willingness 105 4.2762 .58004
Usefulness 105 4.3619 .55684
Like To Use Smartphone 105 4.0190 .85464
Reduce time to find a parking space 105 4.2571 .60492
Quick Payment 105 4.2571 .69377
Widely Used In The Future 105 4.0476 .72564
Valid N (listwise) 105
0 02
9
7
0
1
2
3
4
5
6
7
8
9
10
StronglgUnsatisfied
Unsatisfied Neutral Satisfied StronglgSatisfied
Users satisfaction Of smart parking systems
people
38
Difficult to find parking spaces
First of all, participators whether consider without using smart parking systems to find
a parking space is a difficult thing. For this issue, in total 105 samples, the mean value
was 3.9333 close to 4 (Agree). Thus, for this issue, researcher can say that
participators agree without using smart parking systems to find a parking space is a
difficult thing.
On the other hand, researcher took into account the effect of driving age may be affect
the opinion of finding a parking space issue. Thus, the table 2 compared the mean
value of different driving age groups in difficult parking. As can been seen that the
driving age was less than 1years and 6-10 years two groups had the same mean value
(3.8500), however, the standard deviation (SD) in 6-10 years group was (1.13671)
higher than SD in less than 1 years group (0.81273), which mean the driving age at 6-
10 years participators not had very unified opinions on this issues than less 1 years
group. In addition, the driving age at 1-5 years participators had relative higher mean
value (4.0714) in this issue and the SD (0.61573) was also smaller than others, so, this
group peoples considered that finding a parking space is very difficult things. The
possible reason is that this group peoples had less driving experience. In contrast, over
10 years driving age participators gave a 3.9609 mean value for this issue that a
slightly lower 4 (Agree), which possible due to this group participators had more
driving experience as well as were familiar with the traffic situation in Birmingham
area.
Table 2 Comparing different driving age opinion of difficult parking
Driving age Mean N Std. Deviation
Less1year 3.8500 20 .81273
1-5years 4.0714 14 .61573
6-10years 3.8500 20 1.13671
10years+ 3.9608 51 .72002
Total 3.9333 105 .81177
Table 3 clearly showed that along with the average parking time increased, the mean
value of difficult parking quickly increased. Although the mean value of over 20
minutes group had a small reduced, which possible due to low sample numbers. Thus,
39
different average parking time participators had slight different opinion on difficult
parking.
Table 3 Comparing different average parking time opinion of difficult parking
Average parking time Mean N Std. Deviation
Less than 5 minutes
3.2222 9 .97183
6-10 minutes 3.8182 55 .86262
10-20 minutes 4.2778 36 .51331
20minutes+ 4.0000 5 .70711
Total 3.9333 105 .81177
Table 4 compared the mean value from different income participators. As same as the
average parking time variable, along with the income increased, the mean values of
difficult parking increased. Whether different income groups had a significant
difference opinion on this issue will discussed in section 5.3.
Table 4 Comparing different income opinion of difficult parking
Income Mean N Std. Deviation
Less30k 3.8621 58 .75969
30k-50k 3.7200 25 .89069
50k-80k 4.3158 19 .74927
80k+ 4.6667 3 .57735
Total 3.9333 105 .81177
Willingness of using smart parking systems
The second row in table 1, the mean value of willingness to use smart parking system
was 4.2762 higher 4 (agree), indicating people are willing to use smart parking system.
However, people are willing to use smart parking system whether correlated to people
consider without smart parking system to find a parking space is a difficult thing need
to discuss in the section 5.4.
On the other hand, table 5 showed mean values of different income groups of
willingness of using smart parking systems. As can be seen that as incomes increased,
40
the mean values of willingness increased. There had a reasonable inferences that
people who had more incomes maybe had a higher position in their works, and the
time was more valuable and meaningful for them. As a result, those people did not
want to waste time on searching parking spaces, they were willing to use smart
parking system that can save parking time as well as quickly pay the fee. Similarly,
table 6 compared the mean value of different average parking time groups that the
same trend as income variable. So, the effect of income and average parking variables
on willingness of using smart parking systems will detail discussed in section 5.3.
Table 5 Comparing different income opinion of willingness
Income Mean N Std. Deviation
Less30k 4.1897 58 .54473
30k-50k 4.2000 25 .57735
50k-80k 4.6316 19 .59726
80k+ 4.3333 3 .57735
Total 4.2762 105 .58004
Table 6 Comparing different average parking time opinion of willingness
Average parking time Mean N Std. Deviation
Less than 5 minutes
3.8889 9 .78174
6-10 minutes
4.1818 55 .54742
10-20 minutes
4.5000 36 .50709
20minutes+
4.4000 5 .54772
Total 4.2762 105 .58004
However, the table 7 showed the mean value has no significant differences in male
(4.2241) and female (4.3404) on willingness of using smart parking systems, which
are close to the total mean value (4.2762).
41
Table 7 Comparing male and female opinions of willingness and usefulness
Gender Willingness Usefulness
Male Mean 4.2241 4.3966
N 58 58
Std. Deviation .59362 .59056
Female Mean 4.3404 4.3191
N 47 47
Std. Deviation .56247 .51526
Total Mean 4.2762 4.3619
N 105 105
Std. Deviation .58004 .55684
Consider smart parking system is useful
As shown the thirdly row in table 1, the mean value that people consider smart
parking systems are useful was 4.3619 larger than 4 (Agree). Thus, researcher can say
that in this survey, people consider smart parking system is useful. On the other hand,
table 7 indicated that female (4.3191) and male (4.3966) had similar mean value on
this issue.
Like to use smartphone way
The mean value for this issue calculated in fourth row in table 1, it was 4.0190 larger
than 4 (Agree). Similarly, researcher got a conclusion that people like to use
smartphone to find a parking space. On the other hand, table 8 demonstrated that as
the age increased, the mean valued decreased. It indicated that unlike young people,
older peoples’ attitude on using smartphone to find a parking space tend to neutral.
According to Van der Waerden, Borgers, and Timmermans (2006) study, said that
older people were less willing to change driving habits than young people, due to
them were more conservative. In this case, the older people possibilities prefer to use
the traditional parking strategy, such as experience.
42
Table 8 Comparing different ages opinion of using smartphone way
Age Mean N Std. Deviation
17-29 4.1364 44 .95457
30-45 4.1026 39 .64051
46-60 3.8333 12 1.02986
60+ 3.4000 10 .69921
Total 4.0190 105 .85464
Reduce time to find a parking space and quickly payment
The mean values of these two issues is same (4.2571) in table 1, which showed people
had same perception that smart parking system can reduce time to find a parking
space as well as can quickly pay the parking fee. However, the standard deviation (SD)
for these two issues were not equal, the SD for quickly payment (0.69377) higher than
SD for reducing time to find a parking space (0.60492). Thus, in this case, though
these two issues had same mean value, peoples hold controversial attitudes that smart
parking system can quickly pay parking fee compared to smart parking system can
reduce time to find a parking space.
Widely used in the future
For this issue, the mean value was 4.0476 in table 1, indicating people agree smart
parking systems would be widely used in the future.
The Functions Demand for Smartphone Application
This figure 8 showed that the amount of peoples' demand for four functions of smart
parking system. In 105 samples, showing vacant parking spaces location got the most
votes (97). In addition, 77 respondents required smart parking system can display the
parking rates. The number of respondents who selected the navigation function and
reservation were 56 and 36 respectively. It can be seen that the maximum demands
for smart parking system was that showing location of available parking spaces,
followed by the display parking rates and navigation. In addition, the number of
people who choose reserved function is minimal. Such statistic results In line with the
real situation that operation situation of smart parking system in the Birmingham.
SMARTPARKING ltd implemented SMARTPARKING program mainly contains
43
showing nearest available parking spaces and its parking rates, as well as driving can
choose navigation function. (Actual function pictures in Appendix B)
Figure 14 Function demands for smart parking systems
5.2.3 Benefits and Drawbacks of Smart parking systems
The table 9 listed some mean values of smart parking systems’ potential benefits. As
shown that people gave the highest mean values to ‘develop parking policy’
(mean=4.0190) as well as its SD (0.75931) was also the smallest in this table,
indicating people’s opinions were consistency. On the contrary, people choose a
neutral stance that smart parking system can create jobs, due to its mean value was
2.7333 lower than 3 (neutral).
Smart parking systems indeed can help to develop parking policy. For instance, smart
parking systems can monitor parking occupancy status and frequency, which can be
combined with traffic conditions to timely adjust the parking fee. However, People
tend to neutral attitude that smart parking systems can create jobs (mean=2.7333) and
promote economic (mean=3.5238). However, the development of public smart
parking systems are a considerable city construction project which can add many jobs
as well as create new businesses. In addition, people still do not realize parking
problems causing much economic losses. For example, people waste too much time
on searching parking spaces, rather than negotiate, entertainment or shopping. Or due
to parking reasons, the number of tourists decreased.
On the other hand, people have similar perceptions that smart parking systems can
reduce energy consumption (mean=3.7714) and mitigate environmental pollution
97
36
77
56
0
15
30
45
60
75
90
105
Location Reservation Parking rates Navigation
Function demands
people
44
(mean=3.7048). For example, many vehicles cruise on the road looking for parking
spaces would use of extra oil and emissions of carbon dioxide.
Moreover, although people hold neutral attitude (mean=2.9238) that smart parking
systems can reduce accidents. The fact is that vehicles cruising on the road add the
probability of the occurrence of accidents.
Table 9 People’s opinions of potential benefits of smart parking systems
Ease
Traffic
Congestion
Create
Jobs
Reduces
Traffic
Accidents
Promote
Economic
Benefits
Environment
s
Save
Energy
Develop
Parking
Policy
Mean 3.8476 2.7333 2.9238 3.5238 3.7048 3.7714 4.0190
N 105 105 105 105 105 105 105
Std.
Deviatio
n
.90703 .81177 .87371 .80185 .90855 .89073 .75931
The table 5.10 represented the mean values of several statements of smart parking
system’s potential drawbacks. As can be found that most the mean values tend to
neutral attitude that possible due to the low usage rate (18%). As a result that most
participants were not familiar to it as well as cannot give some useful opinions of
smart parking system performance. However, a good finding is that peoples not
consider smart parking system will be inefficient (mean=2.4857).
Table 10 People’s opinions of drawbacks of smart parking systems
Not
Easy
To Use
Process
Complex Inefficiency
Restriction
Of Using
Area
Cost
Too
High
Technical
Deficiencies
Mean 2.8762 3.0952 2.4857 3.0381
3.266
7 3.0381
N 105 105 105 105 105 105
Std.
Deviation .82852 2.03585 .88919 .83117 .6970
6 .74581
45
The factors of hindering the development of smart parking system
Figure 15 demonstrated the respondents' perceptions of some factors that could hinder
the development of smart parking system. In total 105 samples, 70 participants
considered that government support would influence the smart parking systems'
development. The cost of smart parking system was another important factor could
hinder the development of smart parking system, which got 62 votes. For the public
acceptability and technology also got some participants recognized, which got 40 and
32 votes respectively. The purpose of this survey was to investigate people's views of
smart parking system, the findings can help the government decided to development
policies and operators to improve performance of system. For instance, the high
public acceptability can encourage the government decided to develop smart parking
system, as well as peoples' requirements data can provide to operators to improve
services and update technology in order to reduce the costs.
Figure 15 Factors of hindering smart parking systems development
Expectations and suggestions of smart parking systems
In addition, the open-ended question in the end of questionnaire gathers people's
expectations and suggestions towards smart parking systems. Though get rare
feedbacks, these opinions can be summarized as the following points. First of all,
people want such a system is cost effective that includes the construction costs and
using costs. Secondly, people hope it is easily accessed and used. Thirdly, such public
smart parking systems should be wildly installed in big cities. Finally, smart parking
systems require advertising to let more people know it and use it.
70
3240
62
0
15
30
45
60
75
90
105
Government
support
Technology Public
acceptability
Costs
Hinder Factors
people
46
Through researcher experiment, SMART PARKING Ltd is operating smart parking
systems in Birmingham area that are free to use as well as easy to use. However, such
public smart parking systems are operating in Birmingham and in the central London
Borough of Westminster, which not installed in many UK cities. Another most
important thing is that this research found that most respondents did not know such
public smart parking systems have been installed in the city. Thus, smart parking
system needs to be better publicity, particularly by governments.
5.3 Analysis of Variance
In descriptive analysis section, the participators' basic characteristics data have been
demonstrated in some graphs as well as combining these information discussed some
people's views of smart parking systems. In this section, analysis of variance can be
used to verify whether these personal characteristics will affect participators’
perceptions of smart parking systems.
5.3.1 The Impact of personal characteristics on willingness of using SPS
This part discussed whether people’s characteristics have a significant effect on their
willingness of using smart parking systems, testing the hypothesis H2. There have
five dependent variables, including, gender, age, income, driving age and average
parking time. Correspondingly, five sub-hypotheses H2a, H2b, H2c, H2d, and H2e
can be tested in this section. The results of Levene’s test are presented in the
beginning of this section. Moreover, the descriptive of tests have been presented in
Appendix A.
The results of Levene’ test showed in table 11. Age variable P-value is 0.003 that is
less than 0.05, so the variances are significantly different for this variable, leading to
not need to do analysis of variance so the hypothesis H2b is fail. However, the others
variables P-value are larger than 0.05 that can do the analysis of variance.
Table 11 Test of Homogeneity of variances (willingness)
Variables Levene
Statistic df1 df2 Sig.
Gender .163 1 103 .688
47
Age 5.000 3 101 .003
Income .310 3 101 .818
Driving age .597 3 101 .619
Average parking time 1.283 3 101 .284
Gender
For this variable test, the null hypothesis was that the mean willingness of the gender
groups (male, female) were equal in the populations in this survey. The null
hypothesis did not involve pairwise comparisons of mean willingness between the
groups. Correspondingly,the alternative hypothesis stated that the mean willingness
of participators in the two gender groups were not equal in the same population. It
was not specified whether one-gender group had a better mean willingness when
compared with another. As can be seen in table 12, the calculated F value was 1.044,
which was closer to 1. Thus, the mean difference between the groups was not
statistically significance. In addition, the reported P value for this statistical test was
0.309 > 0.05. Therefore, there was no evidence to reject the null hypothesis. In other
word, the two gender groups had a common willingness mean value in the samples.
Thus, in this survey, there is no evidence to prove that the gender variable could affect
the people's willingness of using smart parking system, rejecting the hypothesis H2a.
Table 12 Gender variable affects the willingness of using SPS
Sum of
Squares df Mean Square F Sig.
Between Groups .351 1 .351 1.044 .309
Within Groups 34.639 103 .336
Total 34.990 104
48
Income
For income variable test, the null hypothesis was that the mean willingness of the
incomes groups (less than 30k, 30k-50k, 50k-80, 80k+) were equal in the populations
in this survey. Correspondingly, the alternative hypothesis stated that the mean
willingness of participators in the four incomes groups were not equal in the same
population. As shown in table 13, the calculated F value was 3.144, which was much
higher than 1. Thus, the mean difference between the groups was statistically
significance. In addition, the reported P value for this statistical test was 0.028 < 0.05.
Therefore, for this variable, there was evidence to support alternative hypothesis
rather than null hypothesis, which the four incomes groups not had a common mean
willingness in the samples. In conclusion, the income variable possibly affects
participator’s willingness to use smart parking system. Combining with table 5 that
different income groups’ mean willingness, thus, the higher income people are more
willing to use smart parking system, supporting hypothesis H2c.
Table 13 Income variable affects the willingness of using SPS
Sum of
Squares df Mean Square F Sig.
Between Groups 2.989 3 .996 3.144 .028
Within Groups 32.002 101 .317
Total 34.990 104
Driving age
For driving age variable test, the null hypothesis was that the mean willingness of the
driving age groups (less than 1 years, 1-5 years, 6-10 years, and 10 years+) were equal
in the populations in this survey. Correspondingly, the alternative hypothesis stated
that the mean willingness of participators in the four driving age groups were not
equal in the same population. As shown in table 14, the calculated F value was 0.140,
which was closer to 1. Thus, the mean difference between the groups was not
statistically significance. In addition, the reported P value for this statistical test was
0.936 > 0.05. Therefore, there was no evidence to reject the null hypothesis that the
four driving age groups had a common mean willingness in samples. In conclusion, in
49
this survey, there is not enough evidence that the driving age factor possible affect
people’s willingness of using smart parking systems, rejecting hypothesis H2d.
Table 14 Driving age variable affects the willingness of using SPS
Sum of
Squares df Mean Square F Sig.
Between Groups
.145 3 .048 .140 .936
Within Groups
34.845 101 .345
Total 34.990 104
Average parking time
For average parking time variable test, the null hypothesis was that the mean
willingness of the average parking time groups (less than 5 minutes, 6-10 minutes, 10-
20 minutes, and 20 minutes+) were equal in the populations in this survey.
Correspondingly, the alternative hypothesis stated that the mean willingness of
participators in the four average parking time groups were not equal in the same
population. The calculated F value was 4.005 in the table 15, which was much higher
than 1. Thus, the mean difference between the groups was statistically significance. In
addition, the reported P-value for this statistical test was 0.010 < 0.05. Therefore, for
this variable, there was evidence to reject null hypothesis in favor of alternative
hypothesis. Thus, in this survey, this test result can prove that the average parking
time factor possibly affected participator’s willingness to use smart parking system,
thus supporting hypothesis H2e. Combined with table 6 that different average parking
time groups’ mean willingness. There is a conclusion that people who spend more
time on searching parking spaces have strongly willingness of using smart parking
systems.
Table 15 Average parking time variable affects the willingness of using SPS
Sum of
Squares df Mean Square F Sig.
Between Groups 3.720 3 1.240 4.005 .010
Within Groups 31.271 101 .310
Total 34.990 104
50
5.3.2 The Impact of personal characteristics on opinion of difficult parking
This part discussed whether people's characteristics have a significant effect on their
views that without using smart parking system finds a parking space is a difficult
thing, testing the hypothesis H1. As same as above test process, before doing the
analysis of variance, the variances homogeneity tests should be firstly do. Similarly,
the descriptive of tests have been demonstrated in the appendix. As can be seen that
the results of variables Levene’ test in the table 16. The age and driving age variables’
P-value were 0.025 and 0.032 respectively that both less than 0.05, thus, the variances
are significantly different for this variable that not need to do analysis of variance as
well as the hypothesis H1b and H1d are fail. Except age and driving age, others three
variables can do the analysis of variance.
Table 16 Test of Homogeneity of variances (difficult parking)
Variables
Levene Statistic df1 df2 Sig.
Gender 2.532 1 103 .115
Age 3.243 3 101 .025
Income 1.497 3 101 .220
Driving age 3.048 3 101 .032
Average parking time 2.005 3 101 .118
Due to the people’ characteristics whether affect their willingness of using smart
parking system has been detailed explained in above, so, this section directly discuss
the test results. As shown in table 17. The gender variable’s F-value (0.999) was
lower than 1, as well as the p-value was 0.320, which were higher than significant
coefficient P-value (0.05). Thus, for this variable had not enough evidence to prove
that gender possible affects the people’s willingness of using smart parking system.
However, income and average parking time variables’ F-value were higher than 1
(3.127 and 3.191) as well as their P-value were less than 0.05, (0.029 and 0.002),
meaning these two variables possible affect people’ perception that without using
smart parking system to find a parking space is difficult thing. In conclusion, the
hypothesis H1a is rejected and hypothesis H1c and hypothesis H1e have been proved
in this section.
51
Table 17 The Impact of personal characteristics on opinion of difficult parking
Sum of
Squares
df Mean
Square
F Sig.
Gender
Between
Groups
Within
Groups
Total
.658
67.875
68.533
1
103
104
.658
.659
.999 .320
Income Between
Groups
Within
Groups
Total
5.825
62.708
68.533
3
101
104
1.942
.621
3.127 .029
Average
parking time
Between
Groups
Within
Groups
Total
9.574
58.960
68.533
3
101
104
3.191
.584
5.467 .002
5.4 Correlation Analysis
The principle and procedure of correlation analysis have been described in Chapter 4.
This section focuses on results analysis and discussion as well as verifies hypotheses
H3, hypothesis H4, hypothesis H5 and hypothesis H6.
Difficult to find a parking space VS Willing to use smart parking systems
As can be seen in table 18, the Spearman's correlation coefficient value was 0.433,
indicating a moderate, positive correlation between these two variables. Therefore, if
people think more difficult to find parking spaces, they would more willing to use
smart parking system. However, in addition to correlation coefficient ( 𝑟𝑠 ), the
significance (P-value) need to be considered, whether there is any or no evidence to
suggest that linear correlation is present in samples. As introduced in Chapter 4, 𝑟𝑠
was the Spearman’s population correlation coefficient then the hypothesis were:
H0: 𝑟𝑠 = 0, i.e. no monotonic correlation presented in samples
H1: 𝑟𝑠 ≠ 0, i.e. monotonic correlation presented in samples
In addition, in this test, the correlation is significant at the 0.01 levels, which showed
under the table. It is mean that even though the 𝑟𝑠 is positive and high magnitude, it
52
still can not concluded these two variables had a positive strongly correlation. As can
be found that the P-value was .000, which is a very strong evidence to support
hypothesis H3, that difficult to find a parking system and willing to use smart parking
system were monotonically correlated in this survey.
A Spearman's correlation was run to determine the relationship between 105
participants’ view of difficult to find parking space and willing to use smart parking
system. There is a moderate, positive monotonic correlation between them ( 𝑟𝑠 =
0.433, n=105, P<0.01), thus support hypothesis H3.
Table 18 Correlation between difficult parking and willingness of using SPS
Difficulty
To
Find
Parking Willingness
Spearman's rho Difficulty
To
Find
Parking
Correlation
Coefficient 1.000 .433**
Sig. (2-tailed) . .000
N 105 105
Willingness Correlation
Coefficient .433** 1.000
Sig. (2-tailed) .000 .
N 105 105
**. Correlation is significant at the 0.01 level (2-tailed).
Willing to use smart parking system VS Thinking Smart parking system is useful
As can be found in table 19, the Spearman's correlation coefficient value of these two
statements was 0.507 and the p-value was .000, which concluded that willing to use
smart parking system and thinking smart parking system is useful were monotonically
correlated in this survey.
In conclusion, A Spearman's correlation was run to determine the relationship
between 105 participants’ view of willing to find parking space and thinking smart
parking system is useful. There is a moderate, positive monotonic correlation between
them (𝑟𝑠 = 0.507, n=105, P<0.01), supporting hypothesis H4.
53
Table 19 Correlation between willingness of using SPS and SPS is useful
Willingness Usefulness
Spearman's rho Willingnes
s
Correlation
Coefficient 1.000 .507**
Sig. (2-tailed) . .000
N 105 105
Usefulness Correlation
Coefficient .507** 1.000
Sig. (2-tailed) .000 .
N 105 105
**. Correlation is significant at the 0.01 level (2-tailed).
Thinking Smart parking system is useful VS Smart parking system can reduce
parking time
As can be found in table 20, the Spearman's correlation coefficient value of these two
statements was 0.569 and the p-value was .000, which concluded that thinking smart
parking system is useful and smart parking system can reduce parking time were
monotonically correlated in this survey.
In conclusion, A Spearman's correlation was run to determine the relationship
between 105 participants’ view of thinking smart parking system is useful and smart
parking system can reduce parking time. There is a strong, positive monotonic
correlation between them (𝑟𝑠 = 0.569, n=105, P<0.01), supporting hypothesis H5.
Table 20 Correlation between SPS is useful and SPS can reduce parking time
Useful
Reduce
Parking
Time
Spearman's rho Useful Correlation
Coefficient 1.000 .569**
Sig. (2-tailed) . .000
N 105 105
Reduce Parking
Time
Correlation
Coefficient .569** 1.000
Sig. (2-tailed) .000 .
N 105 105
**. Correlation is significant at the 0.01 level (2-tailed).
54
Thinking Smart parking system is useful VS Smart parking system can quickly
payment
As shown in table 21, the Spearman's correlation coefficient value of these two
statements was 0.387 and the p-value was .000, which concluded that thinking smart
parking system is useful and smart parking system can quickly payment were
monotonically correlated in this survey. In conclusion, a spearman's correlation was
run to determine the relationship between 105 participants’ view of thinking smart
parking system is useful and smart parking system can quick payment. There is a
weak, positive monotonic correlation between them (𝑟𝑠 = 0.387, n=105, P<0.01),
supporting hypothesis H6.
Table 21 Correlation between SPS is useful and SPS can quickly payment
Useful
Quickly
Payment
Spearman's rho Useful Correlation
Coefficient 1.000 .387**
Sig. (2-tailed) . .000
N 105 105
Quickly
Payment
Correlation
Coefficient .387** 1.000
Sig. (2-tailed) .000 .
N 105 105
**. Correlation is significant at the 0.01 level (2-tailed).
5.5 Discussion
First of all, in this survey, descriptive analysis section identified and showed the
situation of the collected data as well as did a reasonable interpretation and discussion.
Overall, the collected data meets the real situation of society, so the researcher is able
to consider this research is acceptable and valuable. In addition, this section found
some findings. For instance, people are willing to use public smart parking system as
well as they consider the smart parking system is helpful in parking problems. The
biggest benefits of public smart parking system is to help development of parking
policy. On the other hand, research found people accept the way that using
smartphone to inquire, navigation and pay for the parking fee as well as people's
function demands for smartphone applications. In addition, people believe the smart
parking systems will be widely used in the future public transportation as well as
55
consider government support and the cost are factors that could affect the
development of smart parking system.
In analysis of variance section, researcher analyzed the effect of people’s
characteristics on their opinions of smart parking systems, including ‘without using
smart parking system is difficult to parking’ and ‘the willingness of using smart
parking system’. Researcher found that participators' gender variable not had a
significant effect on these two issues. In other words, the differences of gender maybe
not influence their opinions. For instance, male and female have similar willingness of
using smart parking systems. In addition, due to the great difference within age group
in this survey, researcher cannot verify the influence of age variable on participators'
perception. Moreover, participators' driving age variable cannot be tested that whether
influence people’s opinion of difficult to parking, however, it has been verified that
not has an effect on people’s willingness of using smart parking systems. On the other
hand, there had enough evidence that income and average parking time variables
could affect participators’ perceptions on these two issues. Particularly, the average
parking time possibly had a significant impact. Specifically, combining the results of
descriptive analysis section, the higher income population strongly agree that without
using smart parking system is a difficult thing, as the result, they are more willing to
use smart parking system. Similarly, the population who spend more time on
searching parking space consider difficult to find a parking space, so, they are also
more willing to use smart parking system.
Based on inferences, in the correlation analysis section found that ‘people think
without using smart parking system to find a parking space is difficult thing’ has
positively correlated relationship with ‘peoples are willing to use smart parking
system’. This result is consistent with the logic. Moreover, others correlation
hypothesis also have been tested. For instance, smart parking system can reduce the
parking time as well as quickly payment both have a positive correlation with the
opinion that smart parking system is useful.
In conclusion, this research investigated the perceptions of people towards public
smart parking systems as well as tested the research hypotheses. The figure 16
showed the proved hypotheses model.
56
Figure 16 Proved research hypotheses model
5.6 Summary
In this chapter, first of all, the descriptive analysis section roundly identified and
described collected data as well as discussed data’s authenticity and significance.
Secondly, analysis of variance section focuses on analysis participants' personal
characteristics affect two opinions of smart parking system. Finally, correlation
analysis verified the positive correlation of these two opinions as well as others
hypotheses. Through three sections analyzing works, the researcher had a deep insight
in this research and verified the Initial research hypotheses.
57
Chapter 6 Conclusion
6.1 Introduction
The main task of this chapter is to summarize the work in this research as well as
recommendation the future work. This chapter can be divided into three main sections.
The section 6.2 responds to research questions and objectives. The limitations in this
research are presented in section 6.3. The section 6.4 introduces recommendations for
the future work.
6.2 Respond to Research Questions and Objectives
A number of researches discussed and assessed smart parking systems, however, a
few researches aim to investigating people's perception of this new emerging car
parking management. This study researched this subject. Through data collection and
analysis, researcher got the answer of the research question as well as verified the
proposed theory of this research is that people hold positive perceptions towards
public smart parking systems. Specifically, the samples in this survey, people consider
smart parking systems are helpful in parking problems. In addition, people believe
smart parking systems will be widely used in the future public transportation service.
These positive answers supports research theory.
In addition, this research achieved some research objectives. For the first objective,
this research found that people have high willingness of using public smart parking
systems. Secondly, people think the biggest benefits that public smart parking systems
can be brought is helping development of parking policy. Unfortunately, due to the
low usage rate of smart parking system in the sample of this research, researcher
cannot identify the drawback of smart parking system in people's vision. However,
the perception that people do not consider smart parking systems are inefficiency can
be got. The last one, people consider government support and the cost are factors that
could affect the development of smart parking system.
Overall, this research is an interesting and meaningful study. The purposes of this
research have been achieved as well as got some valuable findings.
58
6.3 Limitations in This Research
First of all, this research use face-to-face questionnaire collecting people's opinions of
public smart parking systems. Although questionnaire can gather relatively large
number of samples, however, deeper perceptions of people toward smart parking
systems cannot be got, which can be obtained by interview method. In addition, the
open-ended question in the end of questionnaire got low responses that cannot form a
valid data.
Secondly, the survey location of this research is in Birmingham, where is already
operating public smart parking systems. However, this survey found that the usage
rate of smart parking systems is lower than expected, concerning smart parking
systems' performance information and opinions cannot analysis and discussion.
Moreover, due to participators' age distribution is significant difference in this
research, for instance, over 60 years old participators have low percentage, the effect
of age factor on people's perception of smart parking system cannot be analysis of
variance. As a result, in this research, researcher cannot to prove people's age could
affect their opinions of using smart parking system. Finally, due to researcher's
limited data analysis knowledge, the vision of findings and discussions are not deep
enough.
6.4 Recommendations for Future Work
Although considerable progress has been made in this research, some improvements
are needed in investigate of people’s opinions of smart parking systems. The
recommendation for future work can be discussed from some aspects. First of all, in
addition to Birmingham area, London also has implemented such public smart
parking systems. Moreover, San Francisco and Los Angeles, USA, as well as Wuhan,
CHINA have implemented public smart parking systems. Analysis and comparison
different areas people's perceptions towards smart parking systems can get
comprehensive conclusion. In addition, parking guidance and information (PGI)
system is the early smart parking management system. There exist some researches
about drivers' response to PGI systems. Comparing people's attitude of two types of
parking management system can find change of people's attitude towards smart
parking systems.
59
On the other hand, people's characteristics, such as age, gender, income, driving age,
average parking time are used to analysis the effect perception of citizens towards
smart parking systems in this research, the effect of education level, occupation as
well as trip purpose and trip frequency and others factors should be discussion in the
future work.
Moreover, in addition to descriptive analysis, this research applied analysis of
variance and correlation analysis, however, one-way ANOVA is used to test whether
one control variable for different levels had a significant impact on observed variables
as well as correlation analysis is one part of bivariate analysis. Therefore, some future
work can be concerned about using more complex and powerful data analysis
techniques can be used to discuss people's opinions data.
6.5 Conclusions
This purpose of this research is to investigate the perceptions of citizens towards
public smart parking systems in Birmingham area. Through overview the literature of
smart cities and smart parking system, researcher proposed a theory that people hold a
positive attitude to public smart parking system and established some hypotheses as
well as decided applied deductive approach and quantitative methodology. In this
study, a questionnaire has been designed and implemented to collect data as well as
three statistic methods are used in analysis part to verify the theory and tested
hypotheses, including descriptive analysis, analysis of variances and correlation
analysis. After analysis and discussion, the research question has been answered and
most research objectives have been achieved.
Word count: 14855
60
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Appendix A
Descriptive (Gender)
Willingness
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Male 58 4.2241 .59362 .07795 4.0681 4.3802 3.00 5.00
Female 47 4.3404 .56247 .08204 4.1753 4.5056 3.00 5.00
Total 105 4.2762 .58004 .05661 4.1639 4.3884 3.00 5.00
Descriptive (Age)
Willingness
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
17-
29 44 4.2045 .50942 .07680 4.0497 4.3594 3.00 5.00
30-
45 39 4.4103 .59462 .09522 4.2175 4.6030 3.00 5.00
46-
60 12 4.1667 .83485 .24100 3.6362 4.6971 3.00 5.00
60+ 10 4.2000 .42164 .13333 3.8984 4.5016 4.00 5.00
Total 105 4.2762 .58004 .05661 4.1639 4.3884 3.00 5.00
73
Descriptive (Income)
Willingness
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
less30k 58 4.1897 .54473 .07153 4.0464 4.3329 3.00 5.00
30k-
50k 25 4.2000 .57735 .11547 3.9617 4.4383 3.00 5.00
50k-
80k 19 4.6316 .59726 .13702 4.3437 4.9195 3.00 5.00
80k+ 3 4.3333 .57735 .33333 2.8991 5.7676 4.00 5.00
Total 105 4.2762 .58004 .05661 4.1639 4.3884 3.00 5.00
Descriptive (Diving age)
Willingness
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
less1year 20 4.3000 .57124 .12773 4.0327 4.5673 3.00 5.00
1-5years 14 4.2857 .46881 .12529 4.0150 4.5564 4.00 5.00
6-
10years 20 4.2000 .61559 .13765 3.9119 4.4881 3.00 5.00
10years+ 51 4.2941 .60973 .08538 4.1226 4.4656 3.00 5.00
Total 105 4.2762 .58004 .05661 4.1639 4.3884 3.00 5.00
74
Descriptive (Average parking time)
Willingness
N Mean
Std.
Deviation
Std.
Error
95%
Confidence
Interval for
Mean
Minimum Maximum
Lower
Bound
Upper
Bound
less than 5
minutes 9 3.8889 .78174 .26058 3.2880 4.4898 3.00 5.00
6-10
minutes 55 4.1818 .54742 .07381 4.0338 4.3298 3.00 5.00
10-20
minutes 36 4.5000 .50709 .08452 4.3284 4.6716 4.00 5.00
20minutes+ 5 4.4000 .54772 .24495 3.7199 5.0801 4.00 5.00
Total 105 4.2762 .58004 .05661 4.1639 4.3884 3.00 5.00
Descriptive (Age)
Difficulty To Find Parking
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
17-
29 44 3.8409 .96311 .14519 3.5481 4.1337 1.00 5.00
30-
45 39 4.0256 .62774 .10052 3.8222 4.2291 2.00 5.00
46-
60 12 4.0000 .85280 .24618 3.4582 4.5418 3.00 5.00
60+ 10 3.9000 .73786 .23333 3.3722 4.4278 3.00 5.00
Total 105 3.9333 .81177 .07922 3.7762 4.0904 1.00 5.00
75
Descriptive (Gender)
Difficulty To Find Parking
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Male 58 3.8621 .84704 .11122 3.6394 4.0848 1.00 5.00
Female 47 4.0213 .76583 .11171 3.7964 4.2461 2.00 5.00
Total 105 3.9333 .81177 .07922 3.7762 4.0904 1.00 5.00
Descriptive (Income)
Difficulty To Find Parking
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
less30k 58 3.8621 .75969 .09975 3.6623 4.0618 1.00 5.00
30k-
50k 25 3.7200 .89069 .17814 3.3523 4.0877 2.00 5.00
50k-
80k 19 4.3158 .74927 .17189 3.9547 4.6769 3.00 5.00
80k+ 3 4.6667 .57735 .33333 3.2324 6.1009 4.00 5.00
Total 105 3.9333 .81177 .07922 3.7762 4.0904 1.00 5.00
76
Descriptive (Driving age)
Difficulty To Find Parking
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for
Mean
Minimum Maximum
Lower
Bound
Upper
Bound
less1year 20 3.8500 .81273 .18173 3.4696 4.2304 2.00 5.00
1-5years 14 4.0714 .61573 .16456 3.7159 4.4269 3.00 5.00
6-
10years 20 3.8500 1.13671 .25418 3.3180 4.3820 1.00 5.00
10years+ 51 3.9608 .72002 .10082 3.7583 4.1633 2.00 5.00
Total 105 3.9333 .81177 .07922 3.7762 4.0904 1.00 5.00
Descriptive (Average parking time)
Difficulty To Find Parking
N Mean
Std.
Deviation
Std.
Error
95%
Confidence
Interval for
Mean
Minimum Maximum
Lower
Bound
Upper
Bound
less than 5
minutes 9 3.2222 .97183 .32394 2.4752 3.9692 2.00 5.00
6-10
minutes 55 3.8182 .86262 .11632 3.5850 4.0514 1.00 5.00
10-20
minutes 36 4.2778 .51331 .08555 4.1041 4.4515 3.00 5.00
20minutes+ 5 4.0000 .70711 .31623 3.1220 4.8780 3.00 5.00
Total 105 3.9333 .81177 .07922 3.7762 4.0904 1.00 5.00
77
Appendix B
78
Appendix C
Questionnaire- Citizens’ perception of the use of public smart
parking systems
Dear participants, I am a postgraduate student in information management at
university of Sheffield, currently working on my dissertation research about the
peoples’ view to smart parking system. This questionnaire is part of my course, I
would be grateful if you could spare a few minutes to complete this questionnaire. All
information will be kept confidential and only be used for academic purpose. Many
thanks.
Section 1 Basic questions
1. What is your gender?
Male
Female
2. What age group do you belong to?
17-29
30-45
46-60
60+
3. What annual income group do you belong to? (Pounds)
Less than 30k
30k-50k
50k-80k
80k+
4. What driving age group do you belong to?
Less than1 year
1-5 years
6-10 years
10 years+
Section 2 Opinions about smart parking system
5. I think that without the use of smart parking system to find a parking space is a
very troublesome thing.
Strongly agree 5 Agree 4Neutral 3Disagree 2 Strongly Disagree 1
The smart parking system uses information and communications technologies to accurately guide drivers to vacant parking space as well as can provide reservation and payment functions by smartphone, which simplifies the parking experience.
79
6. In average, without a smart parking system, how long you can find a parking space?
Less than 5 minute
6-10 minutes
10-20 minutes
20 + minutes
7. Have you used smart parking system before?
Yes
No
8. If you used smart parking system before, are you satisfied with smart parking
system? If no, please continue to the next question.
Strongly satisfied 5 Satisfied 5 Neutral 5 Dissatisfied 5 Strongly dissatisfied 5
9. If there provide a smart parking system, would you willing to use it?
Very willing 5 Willing 4 Neutral 3 Unwilling 2 Very unwilling 1
10. I consider that smart parking system will be useful.
Strongly agree 5 Agree 4Neutral 3Disagree 2 Strongly Disagree 1
11. I think that use of smart parking system can reduce the time to find parking.
Strongly agree 5 Agree 4Neutral 3Disagree 2 Strongly Disagree 1
12. I think that use of smart parking system can quick pay parking fee.
Strongly agree 5 Agree 4Neutral 3Disagree 2 Strongly Disagree 1
13. I consider that use of smartphone app to find a parking space is a good way.
Strongly agree 5 Agree 4Neutral 3Disagree 2 Strongly Disagree 1
14.What functions do you want in this application? (Multiple choices)
Show vacant parking spaces location
Reserved parking spaces
Show parking rates
Parking navigation
Other, please specify________
15. For the following statements of the benefits of the smart parking system, please
tick “√” the box that matches your view most closely. (5-Strongly agree, 4- Agree, 3-
Neutral, 2-Disagree, 1-Strongly disagree)
a. Smart parking system effectively eases the traffic congestion.
5 4 3 2 1
b. Smart parking system creates many jobs.
5 4 3 2 1
c. Smart parking system reduces traffic accidents.
5 4 3 2 1
d. Smart parking system promotes economic development.
5 4 3 2 1
e. Smart parking system reduces carbon dioxide emissions that contribute to urban
environment.
80
5 4 3 2 1
f. Smart parking system save energy.
5 4 3 2 1
g. Smart parking system helps the government monitor the traffic situation to develop
a reasonable parking policy.
5 4 3 2 1
Other, please specify__________________________________
16. For the following statements of the drawbacks of the smart parking system, please
tick “√”the box that matches your view most closely. (5-Strongly agree, 4- Agree, 3-
Neutral, 2-Disagree, 1-Strongly disagree)
a. Smart parking system is not easy to use.
5 4 3 2 1
b. The process of using smart parking system is complex.
5 4 3 2 1
c. Smart parking system is inefficiency.
5 4 3 2 1
d. Smart parking system has restriction of using regional.
5 4 3 2 1
e. The cost of smart parking system is too high.
5 4 3 2 1
f. Smart parking system has some technical deficiencies.
5 4 3 2 1
Other, please specify_____________________________________
17. I think the smart parking system will be widely used in the next few years.
Strongly agree 5 Agree 4Neutral 3Disagree 2 Strongly Disagree 1
18. Which of the following factor do you think would hinder the development of
smart parking system?
Government support
Technology
Public acceptability
Cost factors
Other, please specify___________________
19. What are your expectations and suggestions for future smart parking system?
Thanks again for your participation!
81
Appendix D
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83
84
85
86
87
88
89