To investigate perceptions of citizens towards public...

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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

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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

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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

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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

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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,

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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).

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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

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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.

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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

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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.

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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

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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,

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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

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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.

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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

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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

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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

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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

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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.

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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).

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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+

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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

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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

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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

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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

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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,

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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,

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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).

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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.

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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

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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

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(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

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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

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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

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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

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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

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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

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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.

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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

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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.

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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).

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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

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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.

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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.

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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.

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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.

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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

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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

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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

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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

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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

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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

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Appendix B

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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.

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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.

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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!

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Appendix D

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