Dissertation
Transcript of Dissertation
Short-Stay Car Parking Choice
Behaviour A Case Study of Cardiff City Centre
Chao Qi
Student Number: 1302064
MSc Transport and Planning
Supervisor: Dr. Dimitris Potoglou
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Abstract:
Studying individuals’ parking choice behaviour can significantly contribute to the
parking policy making for urban areas. Based on a parking-user survey conducted in
the main short-stay parking places around the Cardiff city centre, this thesis provides
a thorough analysis of individuals’ parking behaviours. Through descriptive statistics
of respondents’ personal and travelling characteristics, an understanding of parking
users’ basic profiles has been gained. From investigating parking users’ perceptions
of the parking service, potential issues with regard to parking pricing, availability,
safety and information clarity have been identified. Relative suggestions have also
been proposed for improving the parking service in Cardiff city centre.
Through the application of chi-square tests, underlying relations across parking users’
profiles have been revealed. Compared with the working population, individuals who
travel to Cardiff city centre for shopping or leisure tend to bring companions with
them and park less frequently. Meanwhile, female parking users are more likely to
travel for shopping or leisure purposes, while male parking users tend to come for
work reasons.
As the core of the thesis, discrete choice models have been developed to acquire
parking users’ sensitivities to parking charge and parking availability. It is found that
£1 increase in parking fare or one-minute increase in searching time can generally
decrease the log odds of continuing to park by 1.492 and 0.226 separately. In
addition, ‘taste variation’ across various parking user groups has also been obtained.
In terms of increases in parking charge, individuals aged 25-44 tend to be more
sensitive, while people who travel with companions show a lower sensitivity.
Compared with working people, those who travel for non-work reasons are found to
be more sensitive to the increase in searching time. The above finding will provide
useful references to the parking policy improvement within the Cardiff context.
(Word Count: 19830)
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Acknowledgements
Thank you to my supervisor Dr. Dimitris Potoglou for his professional guidance and
support throughout the dissertation. I have appreciated his good teaching in the MSc
Transport and Planning which has inspired me both in term of academic knowledge
and of career planning.
Thank you to the Principal Transport Planner Miriam Highgate and all the other
transport experts at Cardiff Council for throughout providing useful information and
suggestions for the research.
Thank you to the Public Affairs & Research Coordinator Carrie Drage and all the
other professionals at the British Parking Association for providing professional
parking knowledge for this thesis. In particular, I greatly appreciate the £1500 John
Heasman Bursary provided by the British Parking Association, which has enabled the
research to conduct a large scale high-quality parking-user survey.
Thank you to transport expert Richard Carr for sharing his valuable experience in the
parking-user survey design. Richard’s precious advice has helped the survey acquire
the most relevant and targeted data sets.
Thank you to my classmates who helped me to conduct the parking-user survey in
the Cardiff city centre. Finally, many thanks to all the respondents who have spent
their precious time supporting this research.
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Table of Contents
Abstract: .................................................................................................................................... 1
Acknowledgements ................................................................................................................... 2
Table of Contents ...................................................................................................................... 3
Lists of Figures and Tables ......................................................................................................... 6
1. Introduction ....................................................................................................................... 9
1.1 Background of the study .............................................................................................. 9
1.2 Motivation of the study ............................................................................................. 13
1.3 Structure of the thesis ............................................................................................... 15
2. Literature Review ................................................................................................................ 16
2.1 Influential features in parking choice behaviour ....................................................... 16
2.2 Parking pricing ........................................................................................................... 20
2.3 Parking availability ..................................................................................................... 23
2.4 Parking policy and public attitudes ........................................................................... 25
2.5 Models applied to analyse parking choice behaviour ............................................... 27
2.6 Improvement of discrete choice modelling .............................................................. 31
2.7 Conclusion of literature review ................................................................................. 34
3. Methodology ....................................................................................................................... 37
3.1 Overview of the Methodology .................................................................................. 37
3.2 Conceptualisation of the study.................................................................................. 38
3.3 Design of the questionnaire ...................................................................................... 39
3.3.1 Profile of parking users ................................................................................... 39
3.3.2 Issues in parking service from users’ prospective .......................................... 41
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3.3.3 Driving forces behind parking choice behaviour and sensitivities to parking
features ................................................................................................................... 42
3.4 Pilot survey ................................................................................................................ 45
3.4.1 Findings from the pilot survey ........................................................................ 45
3.5 Implication of the main survey .................................................................................. 49
3.6 Data analysis methods ............................................................................................... 50
3.6.1 Chi-square test ............................................................................................... 50
3.6.2 Logistic regression .......................................................................................... 50
3.7 Conclusion of the Methodology ................................................................................ 54
4. Data Analysis ....................................................................................................................... 55
4.1 Descriptive and frequencies statistics of parking users’ profiles .............................. 55
4.1.1 Gender and age group .................................................................................... 55
4.1.2 Originations .................................................................................................... 56
4.1.3 Travel purpose to Cardiff city centre .............................................................. 58
4.1.4 Travel group size ............................................................................................. 58
4.1.5 Parking duration ............................................................................................. 59
4.1.6 Parking frequency ........................................................................................... 60
4.1.7 Reasons for parking location choice ............................................................... 61
4.1.8 Searching time for parking spaces .................................................................. 62
4.1.9 Distance to destinations ................................................................................. 63
4.1.10 Park Mark ..................................................................................................... 63
4.1.11 Conclusion of parking users’ profiles............................................................ 64
4.2 Parking users’ perceptions to parking service ........................................................... 66
4.2.1 Rating of parking charge ................................................................................. 67
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4.2.2 Rating of parking availability .......................................................................... 68
4.2.3 Ratings of information clarity and payment options...................................... 69
4.2.4 Ratings of personal safety and vehicle safety ................................................ 70
4.2.5 Conclusion of parking users’ perceptions and relative suggestions............... 71
4.3 Relations across parking profiles ............................................................................... 75
4.3.1 Travel purpose and travel group size ............................................................. 75
4.3.2 Travel purpose and parking frequency ........................................................... 76
4.3.3 Travel purpose and age .................................................................................. 78
4.3.4 Travel purpose and gender ............................................................................ 78
4.3.5 Travel purpose and parking duration ............................................................. 79
4.3.6 Conclusions of relations in parking users’ profiles ......................................... 80
4.4 Logistic Regression .................................................................................................... 81
4.4.1 Choosing frequencies of alternatives ............................................................. 81
4.4.2 Independent samples t-test ........................................................................... 83
4.4.3 Modelling parking users’ general sensitivity to parking features .................. 84
4.4.4. Modelling parking users’ taste variations to parking features ...................... 86
4.4.5 Modelling sensitivities of parking users with various characteristics ............ 91
4.5 Conclusion of the data analysis ................................................................................. 94
5. Conclusions and Recommendations ................................................................................... 96
References: ............................................................................................................................ 102
Appendices ............................................................................................................................ 109
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Lists of Figures and Tables
List of Figures:
Figure 1.1 Study area: the Cardiff city centre
Figure 1.2 Survey location: short-stay parking at Cardiff City Hall (CF10 3ND)
Figure 1.3 Survey location: short-stay parking in St. Andrews Crescent (CF10 3DB)
Figure 2.1 The process of parking choice
Figure 3.1 Main survey location: Cardiff City Hall CF10 3ND
Figure 3.2 Main survey location: St. Andrews Crescent CF10 3DB
Figure 4.1 Percentages of parking users' age groups
Figure 4.2 Percentages of parking users’ origination localities
Figure 4.3 Percentages of parking users' origination natures
Figure 4.4 Percentages of parking users' travel purposes
Figure 4.5 Percentages of travel group size (adults)
Figure 4.6 Percentages of travel group size (children)
Figure 4.7 Distribution of short-stay parking users' parking duration
Figure 4.8 Percentages of individuals' parking frequency
Figure 4.9 Percentages of reasons for specific parking choices
Figure 4.10 Percentages of searching time for parking spaces
Figure 4.11 Percentages of walking time to destinations
Figure 4.12 Percentages of ratings for parking charge
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Figure 4.13 Percentages of ratings for parking availability
Figure 4.14 Percentages of ratings for information clarity on payment machines
Figure 4.15 Percentages of ratings for payment options
Figure 4.16 Percentages of ratings for personal safety
Figure 4.17 Percentage of ratings for vehicle safety
Figure 4.18 Ratings of parking features
Figure 4.19 Choosing frequencies of alternatives
Figure 4.20 Percentages of parking users' choices if they choose not to park at the
current location
List of Tables:
Table 1.1 On street short-stay parking tariffs across city centres in the UK
Table 2.1 Studies on influential factors of car parking choice behaviour
Table 2.2 Studies modelling parking choice behaviour
Table 3.1 Questions and options related to parking users’ profiles
Table 3.2 Q10: Parking users’ prospective to the parking service in Cardiff city centre
Table 3.3 Attributes and levels for the stated preference questions
Table 3.4 Full factorial design result (with blocks)
Table 3.5 Attributes and alternatives for discrete choice questions
Table 4.1 Descriptive statistics of parking users’ basic profiles
Table 4.2 Summarisation of parking users’ ratings to parking features
Table 4.3 Work_or_Not * Companion Crosstabulation
Table 4.4 Work_or_Not * Parking Frequency Crosstabulation
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Table 4.5 Work_or_Not * Agegroup Crosstabulation
Table 4.6 Work_or_Not * Gender Crosstabulation
Table 4.7 Work_or_Not * Parking_duration Crosstabulation
Table 4.8 Independent samples t-test result
Table 4.9 Binary regression result for general parking users
Table 4.10 Binary regression result for parking users with different genders
Table 4.11 Binary regression result for parking users with different travel purposes
Table 4.12 Binary regression result for parking users belonging to different age
groups
Table 4.13 Binary regression result for parking users with different travel group sizes.
Table 4.14 Summarisation of parking user groups’ different sensitivities to parking
features
Table 4.15 Binary regression result for parking users with various characteristics
(step 1)
Table 4.16 Binary regression result for parking users with various characteristics
(step 2)
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1. Introduction
Based on a parking-user survey conducted in Cardiff city centre, this study provides
an analysis of individuals’ parking choice behaviour. Through descriptive statistics,
the study has identified the basic profiles of parking users in Cardiff city centre.
Meanwhile, their perceptions to parking features have also been analysed to
discover the potential issues in the parking service. Using chi-square test, deeper
relations in parking users’ profiles have been explored. It has been found that
generally parking users with different genders and travel group sizes tend to differ in
travel purposes. In addition, people with different travel purposes have shown
variations in parking frequencies. As the core of the thesis, logistic regression
models have been developed to test parking users’ sensitivities to parking pricing
and parking availability in the context of Cardiff city centre. Through modelling the
stated-preference data, it is found that increases in parking pricing and searching
time for parking spaces have definitely adverse impacts on the possibility of
continuing to park. A £1 increase in parking fare or a one-minute increase in
searching time will decrease the general log odds of continuing to park by 1.492 and
0.226 separately. Meanwhile, through adding parking users’ personal characteristics
into the modelling, the study has shown the varied sensitivities across different
parking user groups. In the context of Cardiff city centre, parking users aged 25-44
are more sensitive to the increase in parking charge, whereas travellers with
companions tend to be less sensitive to the changes in parking pricing. Furthermore,
parking users who travel for shopping or leisure purposes have shown a higher
sensitivity to the searching time for parking spaces. Based on the findings from the
analysis, several suggestions for improving the parking policy in Cardiff city centre
are made in the concluding section.
1.1 Background of the study
Parking is a necessary component of motor vehicle travels. Travellers usually attach
great importance to the conditions of parking (Clinch and Kelly 2003). Variations in
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parking features can definitely influence individuals’ choices of parking location or
travel modes (Thompson and Richardson 1996). Understanding the impacts of
parking features can help transport planners to devise more efficient parking policies.
Parking policy is an essential tool for controlling the travel demand in urban areas.
Through parking pricing and parking supply management, such policy can discourage
the private vehicle usage and parking in urban areas (Feeney 1988). Thus, many
issues such as congestion and air pollution in urban areas can be alleviated.
Cardiff city centre, the study area of this thesis, is the central business district (CBD)
of Cardiff, Wales. It is the sixth most successful shopping hub in the UK, with a
shopper footfall of 55 million during 2008-2009(Bolter 2009). For visitors in motor
vehicles, the Cardiff Council provides various facilities to accommodate their parking
demands, such as park-and-ride, off-street car parks and on-street parking (Cardiff
Council, 2014a). Facing the challenge of large footfall, effective parking policies are
essential to the normal operation of Cardiff city centre. Although parking
enforcement to crack down on illegal parking has been applied in Cardiff city centre
(Guardian 2014), without the proper intervention of parking policy tools, congestions
and relative externalities will still jeopardise the sustainability of the city centre
development. As a part of the research, parking experts at the Cardiff Council has
been approached for their current perspectives on the parking policies and
suggestions for this specific thesis.
During the visit to the Cardiff Council, those parking experts communicated that the
Council is currently focusing on improving the on street short-stay parking policy in
Cardiff city centre. In Cardiff, there are three main on-street parking areas: City
Centre, Butetown and Cardiff Bay (Cardiff Council 2014b). In Cardiff city centre, the
long-stay on street parking is mainly provided for working people. People who travel
for shopping or leisure will usually choose the short-stay parking. The Council is
concerned more about improving parking policies for people who come to Cardiff
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city centre for various purposes than for those who come only because of work. Thus,
the Council has recommended that this certain thesis should study the parking
choice behaviour of short-stay parking users in the context of Cardiff city centre.
Figure 1.1 Study area: the Cardiff city centre
Source: http://en.wikipedia.org/wiki/File:Cardiff_UK_location_map.svg
https://maps.google.co.uk/maps/ms?ie=UTF8&oe=UTF8&msa=0&msid=117253141803189732484.00047fcaf6
8f6f0a33950&dg=feature
The study is also supported by the British Parking Association (BPA). BPA has
provided a £1500 John Heasman Bursary for this thesis as the research fund (BPA
2014a). With the help of this bursary, the study is able to hire assistants to help
conduct a larger scale survey. Inspired by the Cardiff Council and the BPA, a
face-to-face parking user survey was conducted in July, 2014. This survey comprised
a total of 233 respondents from two on street short-stay parking places in Cardiff city
centre: Cardiff City Hall (Figure 1.2) and St. Andrews Crescent (Figure 1.3). A
satisfying data set related to individuals’ parking behaviour has been collected and
this will contribute to both this thesis and the future studies.
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Figure 1.2 Survey location: short-stay parking at Cardiff City Hall (CF10 3ND)
Source: The Author
Figure 1.3 Survey location: short-stay parking in St. Andrews Crescent (CF10 3DB)
Source: The Author
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Meanwhile, according to the previous work by Cardiff Council, the charge of on
street short-stay parking in Cardiff is relatively modest compared with city centres
across the UK (Table 1.1). In this thesis, discrete choice models will be applied to
acquire parking users’ sensitivities to the parking charge increase under this
currently modest tariff. In addition, individuals’ sensitivities to the parking availability
will also be obtained through the modelling.
Table 1.1 On street short-stay parking tariffs across city centres in the UK
Parking Time
City Centre
1h 2h 4h 5h
Westminster £3.25 £6.50
Hounslow £1.25 £2.60 £9.50
Birmingham(inner) £1.50 £3.75
Leicester £1.58 £2.15
Nottingham £2.00 £2.00 £6.25
Bristol(max stay 2 or 6
hours)
N/A £3.50 £7.00 N/A
Cardiff £1.70 £2.80 £3.60
Source: Cardiff Council
1.2 Motivation of the study
Studying parking behaviour is fundamental to the design of targeted parking policies
for the sustainable development of urban areas. However, it seems that very little
specific parking research has been done in the context of Cardiff. The Cardiff City
Centre Users Survey has collected some parking data on individuals’ usual parking
types and frequencies of encountering parking problems (Cardiff Council 2007).
However, a lot more is required in order to thoroughly analyse people’s parking
behaviour.
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The first step in studying parking behaviour is acquiring parking users’ basic profiles
which relate to their personal and travel characteristics. The collected information
can help the research understand ‘who the parking users are in the background of
Cardiff city centre’. Meanwhile, parking users’ satisfaction levels to the parking
service can directly influence their willingness to obey the applied parking policies
and rules (Jones 1990). It is important for the research to obtain parking users’
perspectives on the parking service of Cardiff. Through this information, underlying
issues related to several parking features such as parking charge, availability and
parking safety, etc. can be identified in the context of Cardiff city centre. Thus,
relative improvement suggestions can be made for the efficient implementing of
parking policy tools.
As a study which aims to contribute to the parking policy making at Cardiff Council,
the thesis has decided to obtain parking users’ sensitivities to the two most
important parking policy tools: parking charge and parking spaces supply. To achieve
this aim, stated-preference data should be acquired from the parking-user survey
and relative discrete choice models need to be developed. Several studies in terms
of discrete parking choice modelling have been conducted in different areas such as
CBD of Edmonton, CBD of Oregon and CBD of Sydney (Hunt and Teply 1992; Hess
2001; Hensher and King 2001). However, in the context of Cardiff city centre, the
study in this domain is still blank. Meanwhile, the results of these studies may not be
suitable for the parking policy making for the Cardiff locality. This is because
sensitivities to parking features will vary across different regions (Hess and Polak
2004). Parking users in Cardiff will have their own specific sensitivities under the
certain local conditions. Hence, this study will be innovative and meaningful in filling
the gap in parking choice behaviour modelling for Cardiff. Moreover, parking users’
characteristics can also influence their sensitivities against the changes in parking
features. With the help of data on parking users’ profiles, the thesis will try to
achieve this taste variation across different parking user groups. The main findings of
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the study will provide important references for the parking policy revision and
improvement in the context of Cardiff city centre. Three main research objectives
can be summarised as the follows:
What is the profile of short-stay parking users in Cardiff city centre?
What are the current parking issues in Cardiff city centre from the users'
perspective?
What are the drivers for people’s parking choice behaviour and degrees of
people’s sensitivities to different parking features?
1.3 Structure of the thesis
Chapter 2 will provide a critical literature review of previous studies. A wide range of
research in terms of influential factors on parking choice behaviour, parking charging,
parking availability and discrete choice models will be reviewed. The study has
identified the gaps in these studies and will try to fill them in this thesis. The Chapter
3 will illustrate the methodology applied for this specific research including the
conceptualisation of the research objectives, the design process of the parking-user
survey, the implication of the main survey and rationales behind the adopted
discrete choice models. As the core of the thesis, Chapter 4 provides a thorough data
analysis in order to study the parking choice behaviour in the context of the Cardiff
city centre. Descriptive statistics has been applied to obtain parking users’ profiles
and their perceptions to parking service. Chi-square tests have been conducted to
explore underlying relations across individuals’ travel characteristics. Additionally,
discrete choice models have been developed to achieve individuals’ sensitivities to
the changes in parking charge and availability. Taste variations across different
parking user groups are also obtained in this chapter. Finally, the Chapter 5 will
summarise the main findings of the study and will give specific suggestions for the
parking policy improvement in Cardiff city centre. The limitations of this research
and relative recommendations for future studies will also be demonstrated in the
final chapter.
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2. Literature Review
2.1 Influential features in parking choice behaviour
It is necessary to understand what features can influence motorists’ parking choice
behaviour for urban planners to design more targeted and efficient parking policies.
Parking policy acts an essential part in contemporary travel demand controlling
measures for urban transport planning. An effective parking policy can help reduce
the private car usage in city centres and encourage people to use sustainable modes
such as public transport and cycling. Therefore, it is meaningful in terms of
alleviating traffic congestion, air pollution and improving residence quality for city
centres (Feeney 1988; Hess and Polak 2004).
In practice, parking choice is a complex process and is influenced by many factors. It
has been asserted by Thompson and Richardson (1996) that parking choice
behaviour could be considered as a search-process in which parking users make a
series of relative decisions based on their own experience and the conditions
provided by a specific parking place. When a motorist is deciding whether or not to
park at the usual parking place, he/she will firstly evaluate the conditions of the car
park. In this period, many factors can influence a parking user’s decision. These
include travel time to parking places, availability of parking spaces, ease of entering
or exiting the parking lot, walking time to destination, the distance to pay machines,
pricing. Meanwhile, a motorist’s characteristics such as gender, age and travelling
group, etc. will also have impacts on parking choices (Young 1986; Waerden et al.
2003). Dissatisfied parking users will try to find another parking place and restart the
above evaluation process (Thompson and Richardson 1996) or choose to use an
alternative mode. Other users who are satisfied with the overall condition of the
parking space after trade-off will choose to park there. Meanwhile, this process can
is illustrated by the Figure 2.1.
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Figure 2.1 The process of parking choice
Source: Thompson and Richardson 1996.
There are also other studies which have been conducted in terms of various
determinants on parking choice behaviour. Feeney (1988) has reviewed several
mode choice models and argues that compared with internal cost such as fuel cost,
parking charges and other out-vehicle costs are more valued by parking users.
Another study conducted by Young et al. (1991) finds that the importance of time
from parking space to destination is weighed approximately 2.5 times more than in
vehicle time which illustrates that motorists commonly prefer to park at locations
closer to their destinations. Similarly, parking charge is also proved more
predominant than other generalised trip costs. A study to examine individuals’
sensitivities to parking cost and walk time from parking place to destination is
conducted in central Toronto by Miller (1993). The result shows that these two
features are determinants for both parking location choice and transport mode
choices.
Yes
Yes
No
Search for parking space
Examine car park
Drive to next car park
Evaluate car park
Determine route to next
car park
Wait
Accept
car park
Car park
available
Park
No
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The above studies have identified several factors that can significantly influence
parking users’ behaviours. However, they fail to compare individuals’ different levels
of sensitivities to various parking features. Tsamboulas (2001) asserts that pricing is
the most influential determinant in parking choice behaviour. Besides parking fare,
walking time to destinations could also influence parking users’ location choices.
However, this research has not included the impact of cruising time on parking
choice behaviour. It is argued that limited research has been conducted on studying
the influences of parking features other than pricing, such as walking distance and
cruising time for parking spaces (Lambe 1996; Tsamboulas 2001). The study by Golias
(2002) has filled this gap to some extent. Although parking price has the most
important impact on peoples’ parking choice behaviour, searching time for an
available parking space and distance from parking place to destination could also
significantly influence parking users’ location choices (Golias et al. 2002).
However, with regard to an efficient parking policy, it is infeasible to take all
influential factors mentioned above into account. Some features relating to parking
users’ micro-behaviour such as distance to pay machine and distance to end
destination vary largely among different individuals and are meaningless as a
summary for designing parking policies for the public. Major features whose
variations are in general initially considered by parking users and can lead to changes
in parking choice behaviour are the favorites of parking policy designers. It has been
pointed out (Feeney 1988) that parking policies mainly contain two aspects: the first
is changing the structure or levels of parking fare and the other is controlling the
supply of parking spaces. Meanwhile, the supply control not only concerns the
physical supply of parking locations, but also includes access restrictions for certain
time periods or specific parking users. Changes in parking users’ behaviour towards
an applied parking policy might be altering parking locations, parking time, travelling
modes or abandoning the trip (Feeney 1988). Influential factors in parking choice
behaviour concluded from above studies are summarised in the table 2.1.
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Table 2.1 Studies on influential factors of car parking choice behaviour
Studies Influential factors on parking choice behaviour
Young (1986) Travel time to parking place
Walking time from parking space to destination
Availability of parking spaces
Available shade
Feeney (1988) Parking charge and other out-vehicle costs
Supply of available parking spaces
Young et al. (1991) Egress time /Walking time to destination
Parking charge
Parking supply restraints/ Duration restrictions
Miller (1993) Parking cost
Walking time after parking
Tsamboulas (2001) Parking pricing (most influential)
Walking distance to destination
Golias et al (2002) Parking charge(most important)
Searching time for parking places
Walking time to destination
Parking duration
Waerden et al (2003) Distance to the ticket machine
Ease of ingress/egress
Distance to destination
Personal characteristics (minor)
Moreover, Hess and Polak (2004) have proved the existence of the ‘taste variation’
among parking users in different regions. This variation can be explained as different
weights valued by motorists on various parking features based on different local
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conditions. For example, there are differences which exist in sensitivities to
searching time for parking spaces between parking users in two UK cities,
Birmingham and Sutton Coldfield (motorists in Birmingham showed higher sensitivity
to search time). Therefore, it is important to have research to work out the specific
‘taste of parking’ of local parking users. Meanwhile, the basic characteristics of
parking users should be acquired to gain a deeper understanding of their parking
choice behaviours (Hess and Polak 2004).
2.2 Parking pricing
The popularity of non-motorised transport modes or sustainable modes with high
capacity such as bus or tram can help reduce the externalities associated with
congestion in urban areas (Johansson-Stenman 1999). However, Bonsall (2000)
argues that common reliance on private cars has caused dilemma for urban
transport policy makers. It has also been proved that improvement in public
transport service is less effective with regard to reducing private car usage (Bonsall
2000). Relative research was conducted in five UK cities by Dasgupta et al. in 1994.
They have found that a 50 percent reduction in public transport fares would reduce
car usage by only 1-2 percent, while doubling the parking charge could significantly
transfer 20 percent of total car usage in urban areas to public transport or other
modes. It has also been proven (Calthrop et al. 2000) that pricing measures are the
most efficient solution for urban transport demand management (TDM) since pricing
can encourage people to reduce the usage of private cars and move to other
sustainable modes which can ease the traffic burden on roads. Meanwhile, among
different pricing measures, parking pricing is regarded as one of the most powerful
components besides road and congestion pricing (Calthrop et al. 2000). Clinch and
Kelly (2003) also argue that the cost of parking is generally the most important
component of the expenditure generated by an urban car journey (even larger than
oil consumption). Therefore, parking charge could be an extremely powerful tool in
changing motorists’ decisions for mode choice or parking location choice (Clinch and
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Kelly 2003). Moreover, compared with other pricing measures such as road pricing,
parking charging is more easily implemented since it has been recognised generally
and charging facilities are simple and inexpensive. Meanwhile, people tend to be
more averse to road charging or congestion charging and the establishment of
relative charging facilities is much more complex. Therefore, with regard to peoples’
acceptance and to implement costing, parking pricing has its unique advantages
compared with other pricing measures (Arnott et al. 1990; Clinch and Kelly 2003).
Another relative study of the impacts of park-pricing on parking choice and mode
choice has been conducted by Clinch and Kelly (2003) in Dublin. Under the condition
that there is a lack of alternative public transport modes available at that time, most
people choose to change the parking locations when parking fares increase. The
result shows that pricing is a viable policy tool for managing peoples’ parking choice
behaviour (Clinch and Kelly 2003).
The necessity of implementing parking charge has also been argued by Shoup (2011).
It is estimated that 99% of parking places are free in the U.S., which has caused great
problems caused by excess parking behaviours such as oil consumption, air pollution
and congestion. Charging for parking is suggested by Shoup as a way to reducing the
travel demand generated by private cars. Meanwhile, to ease the resistance to the
introduction of charging, Shoup suggested the concept of ‘parking benefit districts’
which would invest the revenues from parking charging in transport improvement
such as the construction of bike paths or the enhancement of public transit services.
This could make residents accept and even welcome the park-pricing policy (Shoup
2011).
However, Calthrop (2002) argues that few sophisticated studies have been done in
terms of on-street parking pricing solutions. Arnott and Rowse (1998) suggest that
the best pricing amount for every parker should be the marginal external cost added
to other parking users. In other words, a parker who has taken up an empty
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on-street parking space has to pay for the additional cost of increased parking spot
searching time for other motorists. Meanwhile, other studies suggest that the pricing
policies should take into account more externalities generated by on-street parking
such as road congestion, air pollution, disadvantages to other transport modes, etc.
Thus, more external costs will be covered by revenues from parking fees (Calthrop
2002; Anderson and Palma 2004). Although these pricing methods make sense
theoretically, the implementation might be difficult since there is no specific scale
and method to calculate this kind of cost.
Moreover, Kelly and Clinch (2006) point out that although altering the parking price
could bring aggregate parking behaviour change as transport planners would hope
for a specific area, the change might cause the extinction of certain travel types
(business or shopping, for instance). This is because travellers for different purposes
should have different sensitivities to the pricing change. At a certain tariff increase
rate (the threshold), the relatively more sensitive groups would cancel their journeys
which could lead to homogeneous trip purpose in a specific urban area (Tsamboulas
2001; Lam et al.2006). Meanwhile, it is of great importance for transport planners to
keep the diversity of trip purposes in city centres. Kelly and Clinch (2006) have
interviewed approximately 1000 on-street parking users in Dublin and have found
that significantly different sensitivities exist between business and non-business
users to parking price. The parking choice behaviour of non-business travellers is
more likely to be influenced by price increase, while motorists travelling for business
purposes present relatively lower sensitivity. Kelly and Clinch suggest for planners
that different pricing schemes should be considered and tested to maintain the
diversity of parking users to city centres. Based on Kelly and Clinch’s findings, it
would be useful to categorise parking users via different standards (gender, travel
purpose, parking frequency, etc.) and find out their sensitivities separately in order
to identify whether significant differences exist among different groups.
23
2.3 Parking availability
Button and Verhoef (1998) argue that the potential advantage of parking policies
derives from the fact that on-street parking is closely related to the road capacity.
Therefore, an efficient parking behaviour management should be beneficial in
reducing congestion. Meanwhile, another important parking feature: searching time,
which can reflect levels of ease of finding an available parking space, should also be
carefully considered by planners. A long searching time for parking space can slow
down the whole operation system of a car park and be a significant contributor to
road congestion (Button and Verhoef 1998). The vacancy rate of a parking place is
partly associated with the parking price, because cheap fares would cause
overcrowding in car parks, especially in busy periods. Laurier (2005) discusses the
issue that motorists in the stage of on-street parking searching usually experience
adverse emotions. Firstly, a driver has to slow down to look for a vacant spot, which
might cause a collision with other vehicles following behind. Secondly, the
distraction could also bring safety risks. Moreover, the driver would tend to be
anxious with the searching time extending, especially when frequently honked by
other vehicles (Laurier 2005). Therefore, parking users will definitely be unwilling to
continue to park at the current location if they find it extremely difficult to find an
available parking space (Button and Verhoef 1998).
Arnott and Rowse (1998) have developed a parking model to test the adverse
impacts generated by searching time for a vacant parking spot. They find that the
‘searching process’ could increase traffic volumes and lower the speed of traffic flow
which would generate externalities such as costs of delayed time. Therefore, besides
parking pricing, planners should also appropriately adjust the provision of parking
spaces to simplify the searching process to attract motorists to park (Arnott and
Rowse 1998). From another perspective, reducing the number of available parking
spots could also be an efficient way to reduce the private car usage in city centres.
However, the premise of applying the limitation still has to guarantee the simplicity
24
in finding an available parking spot for users who choose to continue to park at the
current location.
Shoup (2006) concludes that the general cruising time for an available on-street
parking space in congested urban areas is between 3.5 and 14 minutes on average,
and as many as 74 percent of motorists cannot find parking spaces immediately
during busy periods (Shoup 2006). It is argued that, even though finding a parking
space would be much easier in off-street private car parks, people still tended to
search for a curb space because of the cheaper fares. This finding shows that
motorists will not only consider one single feature when they are making decisions
on whether to park at the current location. In this case, they are likely to take both
price and availability into account and show a stronger sensitivity to price. Shoup has
expressed the concern that inexpensive on-street parking prices would cause
overabundant parkers to choose curb parking, which could largely extend the
cruising time and cause other issues such as congestion and air pollution. It is
suggested that further research should be conducted to help city councils to find the
equilibrium point between on-street parking pricing and parking space supply to
manage the quantity of curb parking users (Shoup 2006). Anderson and Palma (2004)
even regard the search for parking as a major congestion source in urban areas. One
study by them, focusing on short stay parking users (mainly shoppers), has shown
the importance of regular parking pricing in reducing the general cruising time. The
long cruising would not only lead to endogenous congestion of parking users’
vehicles, but also adversely influence other vehicles running on roads which might
cause exogenous congestion to the whole transport system (Anderson and Palma
2004). Therefore, only applying parking space restraint policy without the assistance
of a pricing method to reduce the demand might cause more serious issues and
externality costs to city centres generated by increased cruising time. An efficient
parking policy should combine pricing and parking space supply harmoniously.
25
Meanwhile, from the above literature, it can be illustrated that although parking
pricing and controlling the supply of parking spaces (change level of availability of
parking spaces) are two different methods in urban parking management, they are
closely connected to each other. First of all, as determinants to peoples’ parking
choice behaviour, they both aim to encourage motorists to park at dispersal
locations or change to other transport modes to ease congestion and pollution
issues in city centres. Secondly, increasing pricing rate in a specific location will
definitely cause a reduction trend in the number of parking users which can improve
the availability. Therefore, in terms of on-street parking management, many studies
have considered both pricing and availability aspects because of this strong
connection. In other words, researchers and planners are seeking a balance between
pricing and availability in order to design the optimum parking policies.
2.4 Parking policy and public attitudes
Parking policy concerns the management of parking infrastructure and measures to
control travel demand. As introduced above, two policy tools are mainly
implemented by transport planners: pricing and parking space supply management
(Valleley 1997, p. 105; Feeney 1988). Applying parking policy is a relatively
convenient and inexpensive way to manage car usage in urban areas. Meanwhile, it
can bring substantial revenue for a government. Valleley (1997, p. 139) has proposed
three key principles of successfully implementing a parking policy: (1) the
government should guide and support the parking planning of local transport
authorities to make it coordinate with the integral urban development project. (2)
formulating, operating and enforcing a parking policy unitedly in ‘parking working
groups’ which consist of different transport departments to increase efficiency. (3)
producing a parking plan integrated with urban policy objectives to avoid conflicts
between transport and urban scopes. Though these are useful suggestions, they are
limited to the operation and management of a policy and they neglect other
important aspects: how to increase the efficiency of a parking policy and control its
26
disadvantages. As discussed above, appropriately balancing the connection between
pricing and parking space supply can help manage parking behaviour efficiently.
Meanwhile, the level of public compliance can also determine the effect of parking
policies. Peoples’ perspectives of parking service in terms of charging, information
clarity and safety, for instance, can directly influence their willingness to park at a
specific location and obey the relative rules. Some illegal parking behaviour may be
encouraged under unsatisfying parking conditions such as irrational fare levels and
poor availability of parking spaces. Jones (1990) argues that ambiguous signs and
interpretation on parking facilities could have an adverse impact on parking users’
experiences. Therefore, a study is needed to acquire the levels of satisfaction of
parking uses with regard to various parking features. The relative findings may help
to reveal issues existing in parking service which can adversely influence the
effectiveness of parking policy.
However, the parking policies, in particular the restraint policies, have noticeable
flaws such as the risk of jeopardizing the vitality of city centres, which has caused the
concern with many authorities and retailers. Still and Simmonds (1999) have
examined the impacts of parking restraint policy on the economic prosperity of city
centres in a UK context. They find that shoppers are generally more sensitive to
parking conditions (tariff and availability) than people who travel for work or
business and can freely change shopping locations if they are unsatisfied. Therefore,
many local authorities and retailers think that parking provision in urban centres
should be improved in order to attract more shoppers from competing towns (Still
and Simmonds 1999). However, the finding of another study by Mingardo and
Meerkerk (2012) has rejected the common belief of retailers that higher parking
charges can cause declining profits in city-centre shopping. In contrast, increasing
parking capacity is shown to be positive to the turnover of a regional urban centre
with specific catchment area for car users. Though divergence exists with regard to
the effects of parking charges, improving the availability of parking places close to
27
city centres is recognised as helpful to their prosperity through attracting more
shoppers. To overcome parking policy’s potential risk of city centre prosperity, the
perspectives on parking services and sensitivities to parking features of parking users,
especially shoppers, should be obtained and analysed to assist parking policy
making.
2.5 Models applied to analyse parking choice behaviour
Discrete choice models have been used widely to analyse individuals’ choices among
finite alternatives under different attributes across many areas in transport research.
Discrete choice models can help predict how individuals’ travel behaviour will
change under changes in related attributes. For example, Train (1977) demonstrates
the feasibility to forecast mode choice (bus, car and rail) to work under different
attributes such as family income, cost and walking time, etc. With regard to parking
behaviour study, discrete choice models such as the multinomial logit and nested
logit model have been generally adopted by researchers (Hess and Polak, 2004).
A multinomial logit (MNL) model is developed by Teknomo and Hokao (1997) to
study parking users’ location choice in the Central Business District (CBD) of
Surabaya. They model three choice options: ‘on-road parking, off-street at surface
parking, off-street on multistory parking’. The result demonstrates that availability of
parking spaces, walking time to destination, pricing and comfort etc. can influence
motorists’ choices of parking locations. A similar model has also been used by Spiess
(1996), who has modelled the parking lots choice behaviour with a focus on
park-and-ride users. Hess (2001) conducts another MNL analysis to investigate the
influences of free parking on parking demand and mode choice. Respondents in
Portland’s CBD were offered three options: drive alone, carpool and transit. It is
found that under free parking, as many as 62 percent people would drive alone.
However, when a $6 daily pricing was applied, 21 percent of previous drive-alone
commuters would alter to carpool or transit.
28
Hunt and Teply (1992) conducted a nested logit (NL) model for parking location
choices using data collected in the CBD of Edmonton, Canada. The alternatives of the
model are hierarchically structured so that an alternative can be composed of other
subset alternatives. They design three choice alternatives for respondents: off-street
parking, on-street parking and employer-managed parking. Meanwhile, the off and
on-street parking alternatives consist of several individual locations as subsets.
Distance to work place, parking cost and waiting time, etc. are considered as
attributes which could influence parking users’ choices. The result successfully
proves that the above factors could influence parking choice behaviour, whereas the
research also has limitation in treating all parking users homogenously. Therefore,
Hunt and Teply advise that this model should be conducted separately for different
parking user groups for future research. The NL model is also adopted by Bradley
(1993) et al to examine the impacts of parking policy on mode choice and parking
type choice behaviours. Hensher and King (2001) develop another NL model based
on data from a stated preference in Sydney’s CBD to study the influences of tariff
schedule and operation-hour (availability) on parking choices. The design of
alternatives is highly hierarchical. For instance, the alternative ‘continue to drive and
park’ is classified into drive and park elsewhere in the CBD,at the fringe and beyond
the fringe of the CBD. They assert that changes in parking behaviour in Sydney’s CBD
can be mainly attributed to pricing policy (97%), while the contribution of supply
management only accounts for 3% (Hensher and King 2001).
Hess and Polak (2004) have successfully developed a more advanced model: mixed
multinomial logit (MMNL) in their study. It has made up for the shortage of advanced
choice model applications for previous parking choice behaviour studies. MMNL has
its unique advantages over other discrete choice models. It can help acquire the
random taste variations of different individuals. In other words, MMNL is able to
capture the heterogeneities in parking users’ sensitivities to different parking
features under various backgrounds in terms of income, gender and travel purpose,
29
etc. (Train 2003; Hess and Polak 2004). Thus the result modelled by MMNL is closer
to the practical situation than other modes which assume that all the motorists with
different characteristics share a homogenous ‘taste’ for parking features. Hess and
Polak find that the heterogeneity in motorists’ tastes can lead to significantly
different attitudes with regard to parking features such as tariff, search and egress
time. Parking users’ profiles, such as travel purposes, are proved to be important
factors which affect parking choice behaviour.
Table 2.2 Studies modelling parking choice behaviour
Study Study
Area Alternatives Attributes
Adopted
Model
Hunt and
Teply (1992)
CBD of
Edmonton,
Canada
a. Employer
arranged parking
b. On street parking
with subset
alternatives
c. Off street parking
with subset
alternatives
a. Money cost;
b. Distance to
destination;
c. Trip position
(home-work or
work-home);
d. Nature of
parking
surface;
e. Searching and
waiting time
Nested
Logit Model
30
Teknomo
and Hokao
(1997)
CBD of
Surabaya,
Indonesia
a. On street parking
b. Off street surface
parking
c. Off street
multistory parking
a. Availability;
b. Travel
purpose;
c. Search time;
walking time;
d. Pricing;
e. Safety;
f. Comfortability
Multinomial
Logit Model
Hess (2001) CBD of
Oregon,
US.
a. Drive alone
b. Carpool
c. Transit
a. Cost of parking
(free and not
free);
b. Transit Travel
Time
Multinomial
Logit Model
Hensher and
King (2001)
CBD of
Sydney,
Australia
a. Parking close to
the CBD
b. Parking elsewhere
in the CBD
c. Parking at the
fringe of the CBD
a. Operation
hours;
b. tariff schedule;
c. Walking time
to destination
Nested
Logit Model
Hess and
Polak (2004)
West
Midlands
region. UK
a. Free-on-street
b. Charged-on-street
c. Charged-off-street
d. Multi-story car
parking
e. Illegal parking
a. Searching time
for parking
spaces
b. Ingress/egress
time
(hierarchical
parking behaviors
across different
travel purposes
and
characteristics)
Mixed
Multinomial
Logit Model
31
Although MMNL can help capture taste variations across different parking user
groups, it is complex and has higher requirements for data computation. Other
discrete choice models such as binary logit, multinomial logit and nested logit are
obviously used more frequently because they are simpler and easier to understand.
Meanwhile, the relative inaccuracy of these models is possible to be controlled to
some extent. In other words, some functions of MMNL such as ‘random taste
variation’ could also be achieved through appropriately conducting the data
collection and estimation. Parking users’ profiles such as gender, age, travel purpose
and parking duration can be obtained from background questions in the survey.
During the analysis, respondents’ choices to the discrete choice questions are able to
be classified and grouped according to different profiles and modelled to obtain
specific sensitivities to parking features of a certain age group, travel purpose group.
Thus, the ‘random taste variation’ could also be achieved by other models instead of
MMNL.
2.6 Improvement of discrete choice modelling
The data for discrete choice models generally belong to stated preference (SP), since
respondents need to make their choices from provided alternatives based on their
perspectives to different attributes. Indeed, adopting the SP data can be regarded as
an advantage of choice modelling. Respondents’ preferences can be shown through
their trade-off among hypothetical but plausible situations. Besides SP data, other
parametres underlying background questions (travel purpose, age and gender, for
instance) or revealed preference (RP) questions could act as supplementary to the
discrete choice models, in order to achieve more accurate results with taste
variations.
Many studies have demonstrated the effects of unobserved factors, e.g. respondents’
characteristics on discrete choice modelling. A model that treats individuals’ taste
homogeneously will usually generate less practical results. For example, Hensher
(2001) talks about this kind of limitation of generally used standard models such as
32
multinomial logit (MNL) with regard to estimating the value of travel time savings
(VTTS). In Hensher’s study, it is shown that individuals’ heterogeneity and other
unobserved variances can affect the result of VTTS estimation. Greene et al. (2006)
develop a discrete choice model which integrates the heterogeneity variance of
unobserved effects so as to estimate travellers’ VTTS. The model has contained
individuals’ specific characteristics within the data to achieve taste variances. Greene
et al. argue that this method can create a better model fit to obtain different
sensitivities of various travel behaviours with regard to VTTS. Srinivasan and
Mahmassani (2003) develop another model to study route switching dynamics. The
result shows observed influential attributes on route-switching such as timeliness,
level of service and real time information. Many unobserved factors with regard to
peoples’ different travel behaviours and experiences are also proved to have great
influences. Horsky et al. (2006) develop a model to study consumer choice behaviour
and has demonstrated the importance of combining observed preference data from
stated preference data with the unobserved data related to individuals’ specific
preferences. They argue that the prediction accuracy of the model has been
improved through taking preference variations across households into account. The
result can show the heterogeneity across different respondents.
Compared with hypothetical SP questions, RP questions have the advantage of
revealing respondents’ actual behaviours under practical situations. In the transport
domain, combining SP and RP data appropriately can usually make a survey more
efficient (Ben-Akiva and Morikawa 1990). Hensher and Bradley (1992) assert that the
joint utilization of RP and SP data could complement each other’s weaknesses and
help obtain a better understanding of choice behaviours. On the one hand, SP data
can increase the predictive ability of a RP based model. On the other hand, the
explanatory power of a SP based discrete choice modelling can be improved through
connecting to RP results, which can help towards designing of proper attributes and
33
levels for SP questions and obtaining variations among respondents’ choices
(Hensher and Bradley 1992).
This kind of dual data strategy has been applied in many studies. Adamowicz et al
(1997) combine RP and SP data on recreational location choices to model peoples’
perceptions of environmental quality measures. It is asserted that the RP-SP mixed
model is superior to single SP models and has largely improved the modelling
performance in terms of environmental valuation. Jovicic (1998) conducts a
hierarchical multinomial logit model through the union of SP and RP data for
forecasting the influential factors in good transport mode choice among rail, sea and
lorry. The result successfully proves transport cost and time are more influential than
other features such as safety, delay and frequency, etc. Brownstone et al. (1999) use
mixed stated and revealed preferences data to model people’ choices for alternative
fuel vehicles. The single analysis of SP data shows individuals’ general preferences
among petrol, electric and natural gas vehicles, etc. under various attributes. In
contrast, after mixing the RP data, the study finds great heterogeneity existing in
households’ preferences for alternative-fuel vehicles. Therefore, Brownstone et al.
argue that pure SP models might provide implausible results while the mixed use of
SP and RP might make the forecast more accurate.
From the above studies, in order to obtain more accurate and practical results, the
discrete model should take into account data from background or RP questions to
achieve the heterogeneity among different individuals. With regard to parking choice
behaviour, pricing and availability are well known as most influential factors.
Meanwhile, peoples’ specific characteristics such as gender and age can also affect
parking behaviour, although their influences cannot be captured directly. Obtaining
taste variations across different motorists can help us better understand parking
choice behaviour (Hess and Polak 2004).
34
2.7 Conclusion of literature review
Studies focusing on influential factors in parking choice behaviour are reviewed
initially in this chapter. It is found that various factors with regard to the conditions
of parking places and parking users’ characteristics can have greater or lesser
impacts on parking choices. Among them, three main factors have been identified
from the review: pricing, ease of finding a parking space and walking time from
parking lot to end destination. Meanwhile, in terms of parking policies, transport
planners usually consider from two aspects: parking pricing and parking spaces
supply management. The study is intended to investigate the parking choice
behaviours of short-stay (the long-stay is mainly for working people) on street
parking users in Cardiff city centre to make contributions on the policy level for
Cardiff Council and the British Parking Association (BPA). Therefore, the research will
also mainly focus on the influences of parking charge and parking availability (ease of
finding a parking space). Meanwhile, the information of parking users’ basic profiles
(gender, parking duration and travel purpose, for instance) will also be studied to
identify the reasons for the ‘taste variation’ of parking users to different parking
features in Cardiff city centre.
Based on a series of studies on parking pricing, it can be argued that parking charge
is an efficient method for urban transport demand management. Compared to other
pricing methods such as congestion pricing, parking charge has its unique
advantages in public acceptance and operation expenditure. Parking charge is a
significant component in urban parking policy. Free parking without charging will
cause serious congestions and relative externalities in urban areas. Meanwhile,
many studies have stated the pricing amount should be equal to the cost of
externalities generated by parking behaviour. However, this method might be
practically unfeasible, since this cost is hard to be monetarily quantified. The
research will try to solve this question from another angle: motorists’ sensitivities.
Besides pricing, parking users also attach importance to searching time for an
35
available parking space. Reducing parking spaces supply is able to encourage people
to use sustainable transport modes or change parking location. However, as many
studies have argued, a single implementing of the supply restraint could cause
extended cruising time for on-street parking spaces, which would cause serious
transport externalities for urban areas. Thus, an optimum parking policy should
involve both pricing and supply management. This research seeks to quantify parking
users’ sensitivities with regard to pricing and availability through discrete choice
modelling. The findings will help to identify the mutual relation between them and
provide suggestions for transport planners on using these two features to create an
efficient parking policy for Cardiff.
Moreover, the public attitudes to parking policy also have an impact on the
effectiveness of parking policies. It is important for this research to obtain parking
users’ levels of satisfaction to various features of parking services (parking charge,
safety and clarity of guidance information, etc.) in Cardiff city centre. This can help to
identify the underlying parking issues in Cardiff city centre and to make relative
improvements to make people more satisfied with and willing to support the parking
management policies.
In terms of modelling, the study has reviewed various examples of using discrete
choice models to forecast peoples’ parking choice behaviour. Standard models such
as multinomial logit and nested logit models are applied widely. However, a more
complex mixed multinomial logit model (MMNL) has been shown to achieve more
practical results, since it can obtain the random taste variations of different parking
user groups. The research will try to use basic models to achieve the similar function
as MMNL through jointly utilising data from both stated preference questions and
background questions. Respondents will be classified into different groups by
profiles (travel purpose, gender and age for instance) acquired from background
questions. The sensitivities to parking features of different groups will be estimated
36
separately through modelling to acquire random taste variation. The existence of
heterogeneity variance has been demonstrated by many relative studies. It has been
widely proved that this kind of mixed data usage in discrete choice modelling can
enrich models’ forecast function and achieve practical taste variations across
respondents.
The study will conduct a parking-user survey at main short-stay on street parking
places around Cardiff city centre. The questionnaire for the main survey will contain
both background and stated preference questions. Parking users’ basic
characteristics and their perceptions to parking services in Cardiff city centre will be
summarised at the data analysis stage. A discrete choice model will be developed
using data from stated preference questions to obtain parking users’ general
sensitivities to parking charge and parking availability in Cardiff. Meanwhile, taste
variations of different parking user groups will also be shown with the help of data
from background questions. This study can help to obtain a thorough understanding
of individuals’ parking choice behaviour in Cardiff city centre which will fill the gap
that few previous studies have been conducted for this specific area. In particular,
applying logistic regression analysis to discover influential factors on parking choice
behaviour will be innovative to this specific region. The relative findings will be
helpful for the parking policy making within Cardiff context.
37
3. Methodology
3.1 Overview of the Methodology
The research has adopted a quantitative method which is able to analyse data via
mathematical, numerical or statistical techniques to investigate a social
phenomenon. The quantitative research is able to explain and conclude empirical
observations using mathematical expressions and can discover deep connections
between different factors (Given 2008). The research will initially raise specific
questions and then collect enough numerical data from sampled participants. Finally,
it will try to analyse the collected data to answer the research questions in an
unbiased and objective manner via statistical models.
The study has envisaged both background and stated preference questions in the
questionnaire. The background questions are designed to acquire the basic
characteristics of short-stay on street parking users in Cardiff. Meanwhile, the stated
preference (SP) section has provided discrete choice questions which propose finite
alternatives for respondents to choose under different hypothetical situations (Train
and Winston 2007). In this research, the analysis of SP data can help find attributes
affecting short-stay parking users’ choice behaviour as well as people’s sensitivities
to different parking features (price, convenience).The primary designed
questionnaire has been examined by a pilot experiment prior to the main survey.
Based on the findings from the pilot survey, the research has modified the primary
questionnaire and determined the survey period and locations of the main survey.
The software used to analyse the data of this research is IBM SPSS MODELER (SPSS).
SPSS is a popular program for statistical analysis in social science (Levesque 2007).
The data analysis will be conducted through SPSS using cross tabulation, chi-square
test and logistic regression, etc. The main statistical outcomes will be demonstrated
in the data analysis chapter. This Methodology chapter will first of all introduce the
38
conceptualisation process of the research objectives. Then, the survey design
process will be elaborately illustrated in Chapter 3.3 and 3.4. The implication of the
main survey will be demonstrated in Chapter 3.5. Finally, statistical methods applied
to data analysis will be introduced in Chapter3.6.
3.2 Conceptualisation of the study
Following the literature review, the research initially focused on Cardiff Council as a
source of enquiry into the context of parking in Cardiff city centre. Cardiff council has
provided helpful suggestions for this research. According to their response, on street
short-stay parking around Cardiff city centre is an area worthy to be studied. As
long-stay parking is mainly used by working persons, the council is more concerned
about the parking policy making for short-stay parking users coming to Cardiff city
centre for various reasons, e.g. shopping or leisure. Thus, in terms of this specific
study for short-stay parking users, three research questions are proposed.
What is the profile of short-stay parking users in Cardiff city centre?
What are the current parking issues in Cardiff city centre from the users'
perspective?
What are the drivers for people’s parking choice behaviour and degrees of
people’s sensitivities to different parking features?
Richard Carr, an expert in transport planning, also provides valuable suggestions for
this study. He suggests that the hypothetical choice questions should be more simply
designed since respondents may not want to spend time on understanding difficult
questions with complex attributes and alternatives. Meanwhile, a pilot survey should
be conducted to examine whether the questionnaire is designed appropriately.
During the survey design phase, Cardiff Council and the British Parking Association
(BPA) have also provided invaluable advice for the research. The details will be
illustrated in the following survey design section.
39
3.3 Design of the questionnaire
The questionnaire is designed to help collect data related to the three main research
questions mentioned above. It contains three main sections: (1) Questions for
parking users’ basic profiles (Q1-Q9 and Q11-Q13). (2) A question to obtain parking
users’ perspectives on the parking service in Cardiff city centre (Q10) (3). Discrete
choice questions to observe individuals’ choices under the variations of parking price
and availability (B1-B4). The following paragraphs will introduce the design
procedures and rationales of these three sections separately.
3.3.1 Profile of parking users
The basic profile of short stay parking users in Cardiff city centre includes people’s
travel purpose, intended parking duration, parking frequency, originations, distance
to destinations, number of adults/children they are travelling with, time taken to
find the parking space, age and gender. The relevant variables with corresponding
options to acquire parking users’ profiles are listed in the table 3.1. Meanwhile, all
questions and options are coded with specific numbers to make it convenient for
data entry and analysis in SPSS afterwards.
Table 3.1 Questions and options related to parking users’ profiles
Variables Options
Q1.Locality of origination Locality1a(more specific than Cardiff)
Postcode1b:___________________
Q2. Nature of origination 1. Home
2. Work
3. Other
Q3 Travel purpose to Cardiff city centre 1. Shopping
2. Work/Business
3. Leisure
4.Other_________
40
Q4. Intended parking duration Please write the parking duration
__________
Q5. Reason for parking at the specific
location
1.It is the only one I know
2.Close to destination
3. Easy to find a parking space
4. Reasonable parking price
5.It is safe to park here
6. Other___________
Q6. Time spent to find an available
parking space
1. Immediately upon arrival
2. Or record time (minutes)
___________
Q7. Parking frequency 1. Every weekday
2. 2-3 times a week
3. Once a week
4. 2-3 times a month
5. Every fortnight
6. Once a month
7. This is the first time I visit Cardiff city
centre.
Q8. Distance (walking time) to
destination
Please write here:________________
Q9. Size of travel group Adults9a(note number) _____________
Children9b(note number)
_____________
Q11. Gender 1. Male
2. Female
Q12. Age group 1.17-24 2. 25-34 3. 35-44
4. 45-55 5. 55-65 6. Over 65
41
3.3.2 Issues in parking service from users’ prospective
To investigate issues underlying the parking service around Cardiff city centre, a
question concerning people’s levels of satisfaction of several parking features has
been designed. The parking features include parking charge, ease of finding a parking
space, clarity of information on pay and display machines (information on pricing,
length of stay, etc.), range of payment options, personal safety and vehicle safety.
The rating method is adopted and respondents will be asked to score about their
levels of satisfaction for every aspect listed above from 1 to 5 (5 being very satisfied
and 1 being very dissatisfied). The following data analysis will summarise the score
for each aspect and will find out which aspect has the lowest/highest average value.
It is obvious that potential issues are likely to exist in the generally lower-rated
aspects. Therefore, the research has recorded every respondent’s reasons for why
low ratings are given to specific options in the main survey. Through summarising
these reasons, potential issues underlying the parking service in Cardiff city centre,
as well as relative improvement solutions, will be revealed.
Table 3.2 Q10: Parking users’ prospective to parking service in Cardiff city centre
Aspects Rate Reasons for low rating
Parking charge
Ease of finding a parking space
Clarity of information on pay and display
machines e.g. pricing, length of stay, etc.
Range of payment options
Personal safety
Vehicle safety
42
3.3.3 Driving forces behind parking choice behaviour and sensitivities to
parking features
In this section, discrete parking choice questions are designed to acquire stated
preference data. Based on people’s choices under various combinations of parking
charge (increase in parking charge) and availability (time to find a parking space),
logistic models can be developed to investigate how these two parking features
motorists’ parking choice behaviour. Meanwhile, developing the model for different
parking user groups classified by travel purpose, gender, and age, etc. can help to
show their different sensitivities (taste variations) to changes in parking conditions.
Attributes and levels:
From the literature review chapter, it is concluded that an efficient parking policy
should focus on managing two parking attributes: parking charge and parking supply.
Cardiff Council has provided suggestions for the levels of attribute ‘parking pricing’.
Instead of providing a specific parking tariff to respondents, it is suggested that an
‘increase in parking price’ should be used to simplify the situation that different
parking lots have different charging schemes. There are six levels with regard to
parking price increase: £0.50, £1.00, £1.50, £2.00, £2.50 and £3.00. With regard to
parking availability, the research envisages four levels for ‘time spent to find a
parking space’: immediately (0 minute), 2 minutes, 4 minutes and 6 minutes. The
rationality and scientific nature of designed attributes and levels have been tested in
the pilot survey prior to the main survey. The result suggests that all attributes and
levels are appropriate and can reflect the practical situation.
Table 3.3 Attributes and levels for the stated preference questions
Attributes Levels
Increase in parking price £0.50, £1.00, £1.50, £2.00, £2.50, £3.00
Time to find a parking space immediately, 2 minutes, 4 minutes ,6 minutes
43
Full factorial design and block:
The full factorial design is a statistics method which can create all possible
combinations of factors (attributes) and levels. It is required that the design must
consider at least two factors which consist of several discrete levels separately (Box
et al. 2005). In this case, the research has designed two attributes with discrete
levels for stated-preference questions. Therefore, it is feasible to use full factorial
design to obtain all possible combinations of attributes and levels.
There are in total 24 combinations in this discrete choice model design. It is
necessary to include every combination into stated-preference questions which
means there should be 24 hypothetical questions in the questionnaire. It is
unfeasible to include all the 24 questions into a single questionnaire, since it would
make the survey lengthy and respondents would be unwilling to participate.
Therefore, ‘Blocking’ should be applied in this case. In statistics, ‘Blocking’ means
arranging experimental combinations into groups/blocks to shorten a questionnaire
and simplify the research (Gates 1995). This study has separated the 24 situations
into 6 groups (blocks) and created 6 versions of questionnaires (BLCK_1 to BLCK_6).
Each version has only four discrete choice questions in the hypothetical section,
which has largely simplified the questionnaire. Meanwhile, four alternatives are
provided for each choice question: ‘Continue to park here’, ‘Park elsewhere’, ‘Travel
by other mode’ and ‘Not make the trip’. The following is one example of designed
stated preference questions in BLCK_1 version questionnaire. All combinations
acquired by full factorial design are listed in Table 3.4.
‘If the cost to park today increased by £1.00 and you could find parking space
immediately, what would be your choice?’
口 1 Continue to park here.口 2 Park elsewhere.口 3 Travel by other mode:
_________. 口 4 Not make the trip.
44
Table 3.4 Full factorial design result (with blocks)
Block Number Increase in parking charge
(£)
Time to find a parking
space (minutes)
1 1.00 0
2.00 2
1.50 6
2.50 2
2 2.00 6
1.50 2
1.50 4
1.00 2
3 2.00 4
3.00 2
0.50 0
2.50 4
4 3.00 4
0.50 4
1.50 0
3.00 6
5 2.00 0
1.00 4
0.50 6
2.50 6
6 2.50 0
3.00 0
0.50 2
1.00 6
45
3.4 Pilot survey
The pilot survey is an experiment that uses a relatively small-scale sample to test the
questionnaire designed for the main survey. It is prior to the main survey and aims at
discovering the potential issues underlying the survey design. Conducting a pilot
survey is helpful to revise the type, format or process of the main research to make it
more efficient (Sincero 2012).
To test the efficiency of the survey designed for short-stay parking users in Cardiff,
this study comprises a pilot survey in several parking places around Cardiff city
centre. There are two main objectives of the pilot survey. The first is to investigate
and determine the appropriate parking places for the main survey. The second is to
discover flaws in the designed survey which enables the research team to improve
the questionnaire and methods for the main survey. The primary questionnaire
(BLCK_1) used in the pilot survey is in Appendix I.
The pilot was conducted on 7th and 9th July 2014 at four main council-managed short
stay parking places near Cardiff city centre: St. Andrews Crescent, North Road,
Cardiff City Hall (City Hall Rd and Museum Ave) and Sophia Gardens (Cathedral Road).
There were two surveyors conducted the pilot from 9:00am to 12:00am each day.
3.4.1 Findings from the pilot survey
1) People generally responded positively to the questionnaire and sometimes
gave more information than what the questionnaire asked. Occasionally,
people refused to participate but this only happened when someone was in a
hurry for work or a meeting.
2) Respondents were able to understand questions well, including the
hypothetical questions in Sections B which we considered might cause
misunderstanding initially.
3) With regard to Question 9 (‘How many adults/children are travelling with you
today?’), it is thought that this question could cause misunderstanding,
46
resulting in many alone travellers still answering ‘1’. Therefore, it is decided
that this question should be altered to ‘Including you, how many
adults/children are travelling with you today?’ in the main survey.
4) Question 10 should add another column to record the reasons why people give
low rating to a specific aspect. This would help to identify current issues in the
parking service in Cardiff city centre and make relative solutions.
5) The best locations for the main survey should be at City Hall and St. Andrews
Crescent (maps listed in Figures 3.1 and 3.2), since these two parking places
were always busy during the pilot survey and plenty of respondents were
obtained. North Road did not seem an appropriate place, since few cars parked
there. The Sophia Gardens location was mixed with long-stay and short-stay
parking, which would bring difficulty to a survey for short-stay parking users.
Meanwhile, most people chose to park there for access to work in the nearby
offices, thus preventing the research from obtaining respondents coming for
shopping or leisure. Therefore, Sophia Gardens was also omitted from the main
survey locations.
6) In terms of survey time, it was found that all parking spaces were relatively
quiet during 8:30am to 9:30am and only respondents coming for work were
obtained in this period. The busiest period was 9:30am-11:00am and
respondents travelling for various purposes (work/business, shopping and
leisure) were obtained. After 11:30am, fewer cars parked since most parking
spaces were filled up. Meanwhile, in the afternoon, people tended to come to
the parking spaces to leave Cardiff city centre instead of arriving. Therefore, it
was decided that the main survey would not be conducted at this period. Based
on these findings, the main survey was determined to be conducted at
9:00am-12:00am (mainly focusing on 9:30am-11:30am to obtain respondents
coming for various reasons).
47
7) The British Parking Association suggested adding a simple Yes/No question in
the questionnaire: Have you heard of ‘Park Mark’? Managed by the BPA, Park
Mark is a safer parking scheme which aims to reduce crime activities in parking
facilities. Parking operators who take measures to prevent criminal behaviours
have the opportunity to be awarded ‘Park Mark®’ (BPA 2014b). Through this
question, the study could help show the popularity of this safety scheme
among general short-stay parking users.
8) During the pilot, each research personnel managed to obtain approximately 10
respondents each day. So it was predicted that the main survey could achieved
200-240 respondents with the help of four hired surveyors conducting the
research on six weekdays.
9) A prior analysis was conducted for the stated preference data from pilot. The
aim was to test whether attributes and levels were designed rationally. The test
successfully proved the hypothesis that as parking price and searching time
increase, people tend not to park at the current location. The binary regression
result also demonstrated a rational trend in general parking behaviour change
against variations in parking charge and availability (
Meanings of the coefficients can be seen in Chapter
3.6.2).
48
Figure 3.1 Main survey location: Cardiff City Hall CF10 3ND
https://www.google.co.uk/maps/place/Cardiff+Register+Office/@51.4851523,-3.1789675,193m/data=!3m1!1
e3!4m2!3m1!1s0x0:0x230a8c559bb66e0
Figure 3.2 Main survey location: St. Andrews Crescent CF10 3DB
https://www.google.co.uk/maps/place/St+Andrew's+Crescent,+Cardiff+CF10+3DB/@51.4851303,-3.17
41938,109m/data=!3m1!1e3!4m2!3m1!1s0x486e1cb9a12cc25f:0xf84e462d50a83f51
49
3.5 Implication of the main survey
In terms of data collection, the main survey obtained respondents at two
council-managed short-stay parking places near Cardiff city centre: Cardiff City Hall
and St. Andrews Crescent. The survey lasted six weekdays in July 2014. Simple
random sample method was adopted. It was guaranteed that each respondent had
been chosen entirely by chance during the whole process. Each parking user in the
survey areas had the same possibility of being chosen or not which ensured the
analyse result unbiased (Yates et al. 2008). The main survey obtained a total of 233
respondents.
With regard to survey methods, the study determined to conduct a face-to-face
survey. Compared with other methods, face-to-face is the most suitable one for this
research. This is because parking is short-time behaviour. Only by reaching people
who have just finished parking can the surveyors obtain fresh first-hand data
(destination, reason to park here and satisfaction with the service, for instance).
Meanwhile, a face-to-face survey offers the chance to provide more complex
questions such as hypothetical discrete choice questions to help obtain deeper
discoveries, since surveyors will have the opportunity to explain these relative
complex questions to respondents. This kind of data would be difficult to achieve by
other survey modes such as telephone or e-mail. During the main survey, the
interviewers arrived at the specific short-stay parking locations at arranged time
periods to access respondents. Surveyors read questions and recorded answers
provided by parking users who consented to participate. However, the main
disadvantage of face-to-face survey is money-consuming, since on-the-spot
interviewers need to be hired and paid (Doyle 2003).
The research received support from the British Parking Association (BPA) with a
funding of £1500, which enabled the research to hire four assist surveyors and
obtain a satisfying sample size of 233 respondents in total.
50
3.6 Data analysis methods
In the analysis stage, the data with regard to parking users’ personal and travel
behaviour characteristics will be studied through frequencies, percentages, sum,
mean value and other basic statistical methods to outline a generalised profile of
parking users in Cardiff city centre. Meanwhile, chi-square test and discrete choice
modelling (binary logistic regression) will also be developed to obtain a deeper
understanding of peoples’ parking choice behaviour in the context of Cardiff city
centre.
3.6.1 Chi-square test
The chi-square test belongs to statistical hypothesis test methods to identify
whether the ‘opposite of a hypothesis’ (null hypothesis) is true (Greenwood and
Nikulin 1996). This test can be conducted by SPSS. The significance coefficient
(p-value) stands for the possibility that a null hypothesis is true. If the p-value is less
than 0.05 or 0.1, it can be asserted that the observed result is highly unlikely to
belong to the situation under null hypothesis (Stigler 2008). In other words, the
hypothesis will be correct and there is a significant association between two tested
variables. Therefore, the p-value can be the criterion for judging whether or not a
hypothesis is right.
The study will use chi-square test to discover deeper relations underlying peoples’
parking behaviour, e.g. to examine whether significant parking duration difference
exists across different travel purposes or age groups. A number of hypotheses
concerning this kind of relations will be envisaged and tested through chi-square test
in SPSS. The results will be helpful in forming a better understanding of peoples’
parking behaviour in Cardiff city centre.
3.6.2 Logistic regression
In this research, four alternatives: ‘Continue to park here’, ‘Park elsewhere’, ‘Travel
by other modes’ and ‘Not make the trip’ are provided for respondents. Interviewees
51
will make their choices based on their perceptions to combinations of two attributes:
‘increase in parking cost’ and ‘time to find a parking space’.
Table 3.5 Attributes and Alternatives for Discrete Choice Questions
Subject Attributes Alternatives
Parking Choice
Behaviour in Cardiff
city centre
a. Increase in parking fare
b. Time to find a parking
space
a. Continue to park here
b. Park elsewhere
c. Travel by other modes
d. Not make the trip
Discrete choice models can obtain individuals’ choice preferences among finite
alternatives under different combinations of considered attributes. As an essential
part of discrete choice models, logistic regression is a statistical model for predicting
the probabilities of people making certain choices (Bishop 2006). A discrete choice
model will be developed to analyse the stated preference data and acquire parking
users’ sensitivities to parking charge and availability. The modelling will be
developed based on several rational hypotheses related to the main findings of
previous studies.
For instance, previous studies have found that parking charge and availability can
influence motorists’ parking choice decision (Feeney 1988; Hunt and Teply 1992;
Golias et al.2002). Therefore, one hypothesis could be: as the parking charge and
searching time for available parking spaces increase in a parking space around
Cardiff city centre, individuals tend not to continue to park at this location. Moreover,
Hess and Polak (2004) find that travel purpose and individuals’ characteristics can
also influence parking choice behaviour. Thus, hypotheses related to travel purpose,
age and gender, etc. will also be proposed and tested. This study will try to discover
various influential factors on parking choice behaviour in Cardiff city centre.
52
During the modelling, this study will combine the alternatives ‘Park elsewhere’,
‘Travel by other modes’ and ‘Not make the trip’ into ‘Not continue to park here’
because the survey is not intended to acquire data to identify the drivers underlying
the choices among these three options. Therefore, the model will have two response
variables: ‘park here’ and ‘not park here’, which mean the logit regression will be
binary. The form of a binary logit equation is:
[ ( )] [ ( )
( )]
P = the possibility that a specific case happens
α= the constant of the equation
β = the coefficient of the predictor variables.
X = predictor variables
The meaning of the equation can be interpreted that for one unit change in X, the
log odd of P will increase/decrease by the absolute value of β. With regard to this
study, the ‘P’ represents for the probability of choosing to continue to park at the
current location, thus it can be expressed as Ppark. Meanwhile, the possibility of the
opposite choice: ‘not continue to park here’ can be represented by Pnot_park. Two
attributes: ‘increase in parking price’ and ‘time to find a parking space’ are envisaged
in this case and can be represented by and separately.
Therefore, the specific binary logistic equation for this research should be:
[
] [
]
Ppark = the possibility of ‘continue to park here’
Pnot_park = the possibility of ‘not continue to park here’ (Ppark + Pnot_park = 1)
53
α= the constant of the equation
β1= the coefficient of variable ‘increase in parking price’
X1= variable ‘increase in parking price’
β2= the coefficient of variable ‘time to find a parking space’
X2= variable ‘time to find a parking space’
In terms of odds, the equation can also be rewritten as:
( )
( )
( )⁄
( )⁄
The modelling result through SPSS will identify the value and of α, β1 and β2 as well
as the corresponding significance coefficients. Thus, parking users’ sensitivities to the
attributes pricing and availability can be obtained. Meanwhile, as mentioned above,
to acquire the taste variations across individuals, the model will consider the
influence of peoples’ characteristics on parking choice. For example, another dummy
‘Female’ (1 for Female and 0 for Male) can be added to the equation to observe
whether different sensitivities exist between male and female parking users. To
simplify the expression, ‘Cost’ in the following equation represents the variable
‘increase in parking charge’ and ‘Time’ will stand for the variable ‘time spent finding
a parking space’.
[
]
In this case, ‘Male’ parking users are treated as the reference variable. If the resulted
p-value of or is less than 0.05, it can be proved that there are differences
54
existing between male and female parking users in terms of sensitivities to parking
charge or parking availability (a minus represents a higher sensitivity, while a
plus represents a lower sensitivity). In contrast, insignificant results (p-value > 0.05)
will mean no sensitivity difference exists between different genders.
3.7 Conclusion of the Methodology
This chapter has introduced the methodology used for this study. The
conceptualisation of study objectives is firstly introduced. Then, the survey design
process is illustrated. With regard to the core part of the questionnaire: ‘stated
preference questions’, this chapter has demonstrated the design process of
attributes, levels and alternatives in detail. A full factorial design has also been
conducted to create six versions of questionnaires. Meanwhile, the rationales of
statistics tools, in particular the logistic regression model applied for data analysis,
are also introduced. Based on the findings from the pilot survey and testing results,
the primary questionnaire for pilot experiment has been improved for the main
survey. The finalised main survey questionnaire is listed in the Appendix II (BLCK_1).
In the next chapter, data analysis will be conducted to study peoples’ parking choice
behaviour in the context of Cardiff city centre.
55
4. Data Analysis
This chapter mainly contains four sections. The first section will use descriptive
statistics to introduce the basic profiles of parking users in Cardiff city centre.
Through the analysis, a basic understanding of the respondents’ parking behaviour
can be formed. The second section will analyse respondents’ perceptions to the
parking service in terms of several parking features. The result will help discover the
underlying issues in the parking service of Cardiff city centre. The third section will
explore deeper relations across parking users’ characteristics. Based on hypotheses
testing through chi-square test, several underlying relations in terms of parking users’
profiles can be revealed. The last section will develop discrete choice models to
analyse parking users’ sensitivities to parking charge and parking availability.
Meanwhile, different sensitivities (taste variations) across various parking user
groups are also obtained. The relative findings will be helpful to the parking policy
making in Cardiff city centre.
4.1 Descriptive and frequencies statistics of parking users’ profiles
First of all, parking users’ basic profiles will be analysed with the help of descriptive
statistics. Based on the relative findings, a basic understanding of travellers’ parking
behaviour in Cardiff city centre can be obtained. Meanwhile, the result is also useful
to the development of the following hypotheses testing and logistic regression
modelling.
4.1.1 Gender and age group
The main survey has obtained a total of 233 respondents. Among these parking users,
106 are males (45.5%) and 127 are females (54.5%). In terms of parking users’ age
groups, it is found that individuals aged from 25 to 55 account for the largest part of
respondents (22.7% for 25-34, 29.2% for 35-44 and 20.2% for 45-55). Meanwhile,
younger motorists aged 17-24 take up 12.4% and those aged 55-65 take up 13.3%.
Parking users aged above 65 only account for 2.1% of all respondents (Figure 4.1).
56
4.1.2 Originations
With regard to the localities of parking users’ originations, it is found that among the
233 respondents, most of them are local (184) or from surrounding cities or towns
such as Newport (23), Swansea (7) and Bristol (7). Occasionally, the research heard
from respondents coming from relatively distant places such as Kilmarnock or Essex,
but the frequencies are usually only one in these cases. Thus, the study will code
them together as ‘Others’. Figure 4.2 describes the composition of parking users’
localities.
12.4%
22.7%
29.2%
20.2%
13.3%
2.1%
Figure 4.1. Percentages of parking users' age groups
17-24
25-34
35-44
45-55
55-65
Over 65
57
The study has also acquired information on the nature of parking users’ originations
(home, work or other place). It is found that 87.6% of the parking users come from
home, while 12.4% travel from work or other places (Figure 4.3).
78.97%
9.87%
3.00% 3.00%
5.15%
Figure 4.2 Percentages of parking users' origination localities
Cardiff
Newport
Bristol
Swansea
Others
87.6%
9.4%
3.0%
Figure 4.3 Percentages of parking users' origination natures
Home
Work
Other
58
4.1.3 Travel purpose to Cardiff city centre
In terms of respondents’ main reasons for travelling to Cardiff city centre, 40.3% of
parking users come for shopping, 28.3% come for work or business and 15.0% travel
for leisure (Figure 4.4). Meanwhile, people also travel to Cardiff city centre for
various other reasons. For example, there were 23 respondents who had come to
attend graduation ceremonies at Cardiff University during the survey. The other
reasons include appointments, medical treatment and court appearances, etc.
4.1.4 Travel group size
The survey has provided a question to obtain data on the sizes of parking users’
travelling groups. 58.4% of parking users travel to Cardiff city centre without other
adult companions while the other 41.6% of respondents travel with one or more
adult passengers (Figure 4.5). Meanwhile, most respondents (79.8%) do not travel
with children. The percentages of parking users who travel with 1 or 2 children are
12.9% and 5.6% separately. Only 1.7% of respondents travel with 3 or more children
(Figure 4.6).
40.3%
28.3%
15.0%
16.3%
Figure 4.4 Percentages of parking users' travel purposes
Shopping
Work/Business
Leisure
Other
59
4.1.5 Parking duration
Since the survey is aimed at short-stay parking users, the obtained data on parking
durations are all equal or less than 5 hours (the maximum duration for short-stay
parking in Cardiff city centre). The average parking duration of all parking users is
approximately 3.10 hours. Meanwhile, 74 out of 233(31.8%) users choose to park for
the maximum 5 hours and 101 persons (43.3%) choose to park under 2 hours. Only
58 respondents (24.9%) intend to park for 3-4 hours (Figure 4.7).
58.4% 30.5%
7.7% 3.4%
Figure 4.5 Percentages of travel group size(adults)
1
2
3
4
79.8%
12.9%
5.6% 1.7%
Figure 4.6 Percentages of travel group size(children)
0
1
2
3 or more
60
4.1.6 Parking frequency
In terms of parking frequency, 27.5% of travellers park at the current location at
least once a week. While, the other 72.5% of respondents park less frequently
including 38 (16.3%) visitors stated that ‘this is the first time parking at this specific
parking place’. Parking users whose parking frequency is once a month account for
the largest 37.8% across all the parking frequency categories (Figure 4.8).
39
62
40
18
74
0
10
20
30
40
50
60
70
80
1 or less 2 3 4 5
Frequencies
Hours
Figure 4.7 Distribution of short-stay parking users' parking duration
61
4.1.7 Reasons for parking location choice
To identity parking features which are considered by individuals, the survey has
sought respondents’ reasons for choosing to park at the specific parking location.
According to the result, most individuals (68.7%) choose to park at the specific
parking spaces because of the short distance to their destinations. The reasonable
parking price (15.5%) and ease of finding a parking space (9.9%) are the second and
third most important reasons for respondents’ certain parking choices. Parking
safety (2.1%) seems to be a feature neglected by parking users in Cardiff city centre.
Meanwhile, 3% of parking users claimed that the current location was the only
parking space they know.
3.9%
13.7%
9.9%
18.5% 37.8%
16.3%
Figure 4.8 Percentages of individuals' parking frequency
Every weekday
2-3 times a week
Once a week
2-3 times a month
Once a month
First time
62
4.1.8 Searching time for parking spaces
In terms of parking users’ parking experience, the study has obtained the data about
time taken to find a parking space. The average time respondents spend on parking
spaces searching is 1.48 minutes which illustrates that the current parking availability
in Cardiff city centre is relatively satisfying. A total of 146 (62.7%) parking users
stated that they found their parking space immediately upon arrival, while 71(30.5%)
respondents found a parking space in 5 minutes. Only 16 (6.9%) motorists had to
spend more than 5 minutes searching for an available parking spot (Figure 4.10).
68.7%
9.9%
15.5%
2.1% 3.0% 0.9%
Figure 4.9 Percentages of reasons for specific parking choices
Close to destination
Easy to find a parking space
Reasonable parking price
It is safe to park here
Only car park I know
Other
62.7%
30.5%
6.9%
Figure 4.10 Percentages of searching time for parking spaces
Immediately
1-5 minutes
6-20 minutes
63
4.1.9 Distance to destinations
As mentioned above, as many as 68.7% parking users choose to park at the specific
place because of short distance to their destinations. This finding is also supported
by the data collected from Q8 (‘How long will it take from this car park to your trip
destination by walk?’). The average walking time of all the 233 respondents is only
4.75 minutes. During the survey, 202 (86.7%) respondents stated that it would take
them less than 5 minutes to walk to their trip destinations, while 25(10.7%) would
spend 6-10 minutes to reach destinations. Only 6 (2.6%) persons claimed that the
walking time would be above 10 minutes (Figure 4.11).
4.1.10 Park Mark
The question ‘Have you heard of Park Mark?’ is proposed by the British Parking
Association. Park Mark – The Safer Parking Scheme is an initiative of the Association
of Chief Police Officers. It is aimed at reducing crime and the fear of crime in parking
facilities. A parking facility which has applied measures to prevent crime activities
and met police requirements will be rewarded the safer parking status: Park Mark®.
However, from Figure 4.9, it can be seen that on street short-stay parking users tend
to neglect the safety condition of the parking places. Maybe this can explain why
only 13(5.6%) parking users said they have heard of Park Mark during the survey.
The Park Mark seems not to be popular among short-stay parking users in Cardiff
86.7%
10.7%
2.6%
Figure 4.11 Percentages of walking time to destinations
1-5 minutes
6-10 minutes
Above 10 minutes
64
city centre. There is a possibility that people who use off-street private parking
facilities such as the NCP would pay more attention to the Park Mark. Criminal
activities usually do not happen at places exposed to the public such as on-street
parking facilities. People tend not to worry about their personal and vehicle safeties
when they choose to park at on-street facilities for a short period. This will be
supported by the finding in Chapter 4.2.
4.1.11 Conclusion of parking users’ profiles
From the above paragraphs, a basic understanding of short-stay parking users’
profiles can be achieved in the context of Cardiff city centre. Through descriptive and
frequencies statistics, the profiles of parking users’ travelling characteristics,
including travel purposes, parking reasons and parking duration, etc. as well as
personal characteristics such as gender and age, can be generally formed. The
relative results can help solve the first research objective: What is the profile of
short-stay parking users in Cardiff city centre. The following Table 4.1 summarises
the relative statistics results of parking users’ profiles (the sequence follows the main
survey questionnaire).
Table 4.1 Descriptive statistics of parking users’ basic profiles
Profiles Categories Frequency Percentage
Q1.Localities of
originations
Cardiff
Newport
Swansea
Bristol
Other
184
23
7
7
12
78.97
9.87
3.00
3.00
5.15
Q2.Natures of
originations
Home
Work
Other
204
22
7
87.6
9.4
3.0
65
Q3. Travel Purpose Shopping
Work/Business
Leisure
Other
94
66
35
38
40.3
28.3
15.0
16.3
Q4. Parking duration
(Mean: 3.10 hours)
1 hour or less
2 hours
3 hours
4 hours
5 hours
39
62
40
18
74
16.7
26.6
17.2
7.7
31.8
Q5. Reasons for
parking
Only car park I know;
Close to destination;
Easy to find a parking
space;
Reasonable parking
price;
Safe to park here;
Other
7
160
23
36
5
2
3.0
68.7
9.9
15.5
2.1
0.9
Q6. Searching time
for a parking space
(Mean: 1.48mins)
Immediately
1-5 minutes
6-20 minutes
146
71
16
62.7
30.5
6.9
Q7 Parking
Frequency
Every weekday;
2-3 times a week;
Once a week;
2-3 times a month;
Once a month;
First time parking;
9
32
23
43
88
38
3.9
13.7
9.9
18.5
37.8
16.3
66
Q8. Distance to
destination(Mean:
4.75mins by walk)
1-5 minutes
6-10 minutes
Above 10 minutes
202
25
6
86.7
10.7
2.6
Q9. Travel Group
Size (Adult)
1
2
3
4
136
71
18
8
58.4
30.5
7.7
3.4
Q9. Travel Group
Size (Children)
0
1
2
3 or more
186
30
13
4
79.8
12.9
5.6
1.7
Q11. Park Mark Yes
No
13
220
5.6
94.4
Q12. Gender Male
Female
106
127
45.5
54.5
Q13. Age Group 17-24
25-34
35-44
45-55
55-65
Over 65
29
53
68
47
31
5
12.4
22.7
29.2
20.2
13.3
2.1
4.2 Parking users’ perceptions to parking service
Parking users’ levels of satisfactions regarding the local parking service is a very
important reference to the effectiveness of the parking management. Any
unsatisfying condition related to parking services can adversely influence parking
users’ perceptions and their willingness to obey the parking policies. Thus, it is
significant for this study to acquire the data on individuals’ satisfaction levels to
67
various parking features. The findings can help to reveal the underlying issues and to
propose relatively pointed solutions to improve parking services of Cardiff city centre.
In this case, the survey has recorded each respondent’s rating (from 1 to 5, 1
represents ‘very dissatisfied’ and 5 represents ‘very satisfied’) with regard to their
satisfaction levels to various parking features: parking charge, ease of finding a
parking space, clarity of information on pay machines, range of payment options,
person safety and vehicle safety. Meanwhile, the survey has also recorded parking
users’ reasons for giving low rating to specific features. This can help the research
better perceive the potential issues in parking services.
4.2.1 Rating of parking charge
The average score of parking charge is 3.55. 53.2% of parking users are generally
satisfied with the parking charge in Cardiff city centre and have scored 4(32.2%) or 5
(21.0%). Meanwhile, 15.0% of respondents seem dissatisfied with the parking charge,
including 4.7% users who only rate 1 for parking charge (Figure 4.12). In almost all
cases, parking users prefer lower parking fares. During the survey, some respondents
even stated that the parking should be free. However, extremely low parking charge
or free parking will cause congestion issues and relative externalities to urban area.
A rational parking tariff is of great significance to control the parking demand and
ease congestions (Shoup 2011). Meanwhile, based on the mean score of 3.55, it can
be asserted that the parking pricing for short-stay parking in Cardiff city centre is
relative moderate. Most people (85%) showed understanding for the parking pricing
during the survey.
68
4.2.2 Rating of parking availability
The mean rating of ‘ease of finding a parking space’ is 3.88. The relative higher score
corresponds with the finding that most parking users’ can find a parking space in five
minutes in Cardiff city centre (Chapter 4.1.8). 67.4% of respondents are satisfied
with the parking availability and 21.9% give the average score of 3. 10.7% of parking
users assert that it is sometimes hard to find an available parking space (Figure 4.13).
In spite of the small percentage, respondents’ reasons for the low rating of parking
availability are noticeable. In their opinion, the short-stay parking places will become
quite crowded and inaccessible at around 10.30am. This has also been proved by our
observation during the survey. Some respondents who found a parking space easily
during the survey also stated that things would be different if they arrived later.
Therefore, there is a potential issue on how to accommodate the parking demand of
parking users who reach Cardiff city centre at relative late time periods.
4.7%
10.3%
31.8%
32.2%
21.0%
Figure 4.12 Percentages of ratings for parking charge
1
2
3
4
5
69
4.2.3 Ratings of information clarity and payment options
Two features, clarity of information and payment options, are both related to the
condition of pay machines. The average ratings of them are very close (3.84 for
information clarity and 3.86 for payment options). Meanwhile, the percentage
distributions from scores 1 to 5 are also similar (Figures 4.14 and 4.15). It can be
seen from the results that parking users are generally satisfied with the conditions of
pay machines. However, there are also issues existing with regard to pay machines.
During the survey, 21 respondents complained that the payment guidance on the
machine is confusing and possibly be difficult for first-time users. Meanwhile,
although the machines have the device to support payment by card, about 20
parking users argued that the machines sometimes do not work for cards in practice.
Maintenance of payment machines should be more regular to guarantee card
payment. Moreover, 9 parking users complained that there was no change given for
cash payments, which adversely influenced their parking experience.
2.1%
8.6%
21.9%
33.5%
33.9%
Figure 4.13 Percentages of ratings for parking availability
1
2
3
4
5
70
4.2.4 Ratings of personal safety and vehicle safety
Among all the parking features, safety gets the highest average rating (4.40 for
personal safety and 4.28 for vehicle safety). This result proves that the overall safety
condition of on-street parking around Cardiff city centre is satisfying, whereas it can
be seen that the mean score of personal safety is a bit higher than the score of
vehicle safety. Compared with personal safety, parking users tend to be more
concerned with the vehicle safety. 91% parking users are satisfied with personal
safety in the parking place, whereas the satisfying percentage of vehicle safety is 3.4%
3.9%
9.4%
18.9%
34.3%
33.5%
Figure 4.14 Percentages of ratings for information clarity on payment machines
1
2
3
4
5
3.0%
9.9%
17.2%
38.2%
31.8%
Figure 4.15 Percentages of ratings for payment options
1
2
3
4
5
71
lower (Figures 4.16 and 4.17).Several respondents said that rubs between vehicles
could happen because of the small size of parking spaces. Moreover, 2 parking users
concerned that the absence of CCTV could cause a risk to the safety of their vehicles.
4.2.5 Conclusion of parking users’ perceptions and relative suggestions
The average rating across all features is 3.97 which means parking users are
generally satisfied with the parking service in Cardiff city centre. Among the 6
parking features included in the survey, personal safety has the highest rating of 4.40,
and the personal safety is the second most satisfying parking feature with a rating of
4.28. With regard to the conditions of pay machines, the average score is
0.4%
8.6%
41.2%
49.8%
Figure 4.16 Percentages of ratings for personal safety
2
3
4
5
0.90% 11.60%
46.40%
41.20%
Figure 4.17 Percentage of ratings for vehicle safety
2
3
4
5
72
approximately 3.85. Parking availability is slightly more satisfying with a rating of
3.88, whereas the parking charge gets the lowest score of 3.55.The variations in
ratings for different parking features are shown by Figure 4.18.
According to respondents’ reasons for low rating, several underlying issues can be
seen. About 15% of parking users complain that the tariff of short-stay parking in
Cardiff is expensive. 10.7% of respondents are dissatisfied with the parking
availability. They demonstrate that it is sometimes hard to find a parking space
around Cardiff city centre, especially when they arrive at late time periods. Around
13% of parking users have scored under 3 for the conditions of payment machines.
The most common reason for low rating is that the payment guidance is lengthy and
confusing. According to the findings in 4.1.6, 37.8% of parking uses visit Cardiff once
a month and 16.3% of respondents have stated that it is their first time of parking at
the specific parking place. Thus, the complex guidance will cause inconvenience to
these 54.1% of infrequent travellers who are not familiar with the parking pay
machines in Cardiff city centre. Meanwhile, during the survey, around 20
respondents complained that the payment could not support card payment and no
change was given if they paid by cash. With regard to safety, parking users are
overall not concerned with their personal safety, whereas several respondents worry
3.55
3.88
3.84
3.86
4.4
4.28
3 3.5 4 4.5
Parking charge
Parking availability
Information clarity
Range of payment options
Personal safety
Vehicle safety
Figure 4.18 Ratings of parking features
73
about the vehicle safety since the parking space is too small to ensure that no rubs
happen between vehicles.
Based on the above findings, several suggestions can be made. First of all, a more
customer-friendly parking pay machine would greatly improve users’ parking
experience. The guidance information on payment machines should be simplified. It
will largely shorten the time spent on paying for parking. Meanwhile, more regular
maintenance of pay machines should be conducted to assure the varieties of
payment options. Card payment should be consistently supported since it is
convenient compared to cash payment and many respondents’ prefer using their
cards. Moreover, it would be better if machines offered change during cash payment
for parking users who have not carried enough small change.
With regard to vehicle safety, according to the observations from the survey and
respondents’ reflections, there is indeed a risk of collision between vehicles during
the parking process, especially during busy periods. The small size of parking space
can make parking difficult when the space on the two sides has already been taken
up. One possible solution might be properly increasing the width of parking spaces
to leave more room for parking users. However, it can adversely affect the parking
availability since less available parking space will be provided in that location.
Parking charge and parking availability are special features which usually constitute a
dilemma for parking planners. On the one hand, people from their own perspectives
will always prefer lower charging and a larger supply of parking places. On the other
hand, the parking pricing and supply must be controlled to a certain degree to
encourage a percentage of parking users to use other transport modes. As the most
important tools to manage parking demand in urban areas, they cannot be changed
by totally following parking users’ perceptions. Otherwise, the sharply increasing
parking demand will cause congestion, air pollution and safety issues to city centres
(Anderson and Palma 2004; Shoup 2011). Thus, parking polices related to the
74
variations in parking charge and parking supply should be very carefully thought out.
The findings in this section have only provided the perceptual information with
regard to individuals’ satisfactions. In Chapter 4.4, through logistic regression, the
study will preciously capture how peoples’ parking choice behaviour will change
against variations in these two parking features.
Table 4.2 Summarisation of parking users’ ratings to parking features
Parking features Rating Frequency Percentage
Parking charge 1
2
3
4
5
11
24
74
75
49
4.7
10.3
31.8
32.2
21.0
Ease of finding a parking space 1
2
3
4
5
5
20
51
78
79
2.1
8.6
21.9
33.5
33.9
Clarity of information on pay
machines
1
2
3
4
5
9
22
44
80
78
3.9
9.4
18.9
34.3
33.5
Range of payment options 1
2
3
4
5
7
23
40
89
74
3.0
9.9
17.2
38.2
31.8
75
Personal safety 1
2
3
4
5
0
1
20
96
116
0
.4
8.6
41.2
49.8
Vehicle safety 1
2
3
4
5
0
2
27
108
96
0
.9
11.6
46.4
41.2
4.3 Relations across parking profiles
In this section, the study will try to discover deeper relations underlying parking
users’ profiles. Virtually, instead of being independent, many profiles of parking
users are mutually connected to each other. These connections can be discovered
through statistical methods. Relevant hypotheses will be proposed and then tested
using cross tabulation and chi-square test in SPSS. The collected data will be
appropriately recoded for the analysis. The findings will help form a better
understanding of short-stay parking users’ behaviour in Cardiff city centre.
4.3.1 Travel purpose and travel group size
People travel with different purposes might differ in travel group sizes. It is
hypothesised that individuals who visit Cardiff city centre for non-work reasons are
more likely to travel with companions compared with people who travel for work
purpose. First of all, data on ‘travelling purpose’ (Q3) should be recoded into a new
variable ‘Work_or_Not’ in SPSS for conducting the chi-square test. The options
‘shopping’, ’leisure’ and ‘other’ are combined and coded as 0 which stands for
‘Non-work’ and the option ‘work/business’ is recoded to ‘1’ for ‘Work’. Meanwhile,
76
the data from Q9 in the questionnaire should also be processed. Firstly, ‘Compute
variables’ function in SPSS should be used to add the number of companion children
(variable Q9_CH in SPSS) and number of companion adults (variable Q9_AD in SPSS)
together for each respondent into a new variable ‘Travelgroup’ (Travelgroup =
Q9_CH + Q9_AD). Then, ‘Travelgroup’ will be recoded into a new variable
‘Companion’. Variables in ‘Travelgroup’ that equal 1 will be recoded into 0 which
represents travel alone. The others will be recoded into 1 which means travel with
companions. The test result is shown in the Table 4.3.
Table 4.3 Work_or_Not * Companion Crosstabulation
Companion Total
Travel alone Travel with
companions
Work_or_Not
Non-work Count 51 113 164
Expected Count 71.8 92.2 164.0
Work Count 48 14 62
Expected Count 27.2 34.8 62.0
Total Count 99 127 226
Expected Count 99.0 127.0 226.0
Significant Association. Fisher’s Exact Test. p=0.000.
From the result, significant association can be found between ‘work purpose’ and
‘whether travel with companions’ (p-value equals 0.000). The proposed hypothesis is
correct. It is obvious that parking users who visit Cardiff city centre for work purpose
tend to travel alone, while people travelling for non-work reasons such as shopping
and leisure are more likely to bring companions with them.
4.3.2 Travel purpose and parking frequency
People travelling for different purposes might have significant difference in parking
frequencies. According to common sense, a hypothesis could be proposed that
people driving to Cardiff city centre for work purpose have higher parking
frequencies than individuals coming for other reasons such as shopping or leisure.
The test result for this hypothesis is shown in Table 4.4
77
Table 4.4 Work_or_Not * Parking Frequency Crosstabulation
Parking Frequency
Every
weekday
2-3 times
a week
Once a
week
2-3 times a
month
Work_or_Not Non-work Count 1 15 15 19
Expected
Count
6.5 22.9 16.5 18.6
Work Count 8 17 8 7
Expected
Count
2.5 9.1 6.5 7.4
Total Count 9 32 23 26
Expected
Count
9.0 32.0 23.0 26.0
Significant Association. Chi-Square = 34.100 (df= 6) p=0.000
With regard to this hypothesis, the significance coefficient (p-value) is 0.000 which
indicates that difference in parking frequency exists between individuals who visit
Cardiff city centre for work and non-work purposes. Thus, the hypothesis is right.
From the ‘Expected Count’, it can be referred that parking users for work purposes
tend to park more frequently than the other users for non-work purposes such as
shopping and leisure.
Parking Frequency Total
Every
fortnight
Once a
month
First time to
Cardiff city
centre
Work_or_Not Non-work Count 15 71 31 167
Expected
Count
12.2 63.1 27.2 167.0
Work Count 2 17 7 66
Expected
Count
4.8 24.9 10.8 66.0
Total Count 17 88 38 233
Expected
Count
17.0 88.0 38.0 233.0
78
4.3.3 Travel purpose and age
People belonging to different age groups might tend to differ for travel reasons. It is
hypothesised that relatively younger visitors tend to visit Cardiff city centre for work
purpose while the older ones are more likely to travel for shopping or leisure. Data
on respondents’ age from Q13 should be coded into a new variable ‘Age_group’ for
the test. Respondents aged from 17-34 are coded as 1 in SPSS. Meanwhile, people
aged from 35-55 are coded as 2 and the others aged over 55 are coded to 3. The test
result is listed in the following Table 4.5.
Table 4.5 Work_or_Not * Agegroup Crosstabulation
Age group Total
17-34 35-55 Over 55
Work_or_Not
Non-work Count 62 77 28 167
Expected Count 58.8 82.4 25.8 167.0
Work Count 20 38 8 66
Expected Count 23.2 32.6 10.2 66.0
Total Count 82 115 36 233
Expected Count 82.0 115.0 36.0 233.0
Insignificant association. Chi-square test = 2.547 (df =2). P= 0.280
From the test result, there is no significant correlation found between parking users’
age and travel purpose. The resulted p-value equals 0.280 which is much larger than
the threshold value (0.05) for significant association. Hence, the above hypothesis is
null. People of different age groups are visiting Cardiff city centre for both work and
non-work reasons. No travel-purpose difference exists across different age groups.
4.3.4 Travel purpose and gender
Another interesting hypothesis is proposed that males and females might have
general differences with regard to travel purposes. The association between the
variables ‘Gender’ (Q12) and ‘Work_or_Not’ is cross-tabulated to examine this
hypothesis here.
79
Table 4.6 Work_or_Not * Gender Crosstabulation
Gender Total
Male Female
Work_or_Not
Non-work Count 67 100 167
Expected Count 76.0 91.0 167.0
Work Count 39 27 66
Expected Count 30.0 36.0 66.0
Total Count 106 127 233
Expected Count 106.0 127.0 233.0
Significant Association. Fisher’s Exact Test. p=0.013.
The p-value of the test is 0.013 (less than the threshold value 0.05) which means that
significant differences exist in males’ and females’ travel purposes. Based on the
comparison of ‘Count’ and ‘Expected Count’ value, it can be argued that female
parking users are more likely to visit Cardiff city centre for non-work reasons
(shopping or leisure), while male parking users tend to travel for work or business
reasons.
4.3.5 Travel purpose and parking duration
The last hypothesis is designed to examine whether parking users with different
travel purposes differ significantly in parking durations. The data from Q4 on
respondents’ intended parking duration are coded as the numeric variable ’Park_
duration’ in SPSS with five categories: 1 hour or less, 2 hours, 3 hours, 4 hours and 5
hours. The variables ‘Work_or_Not’ and ‘Parking _duration’ are cross tabulated to
test this hypothesis. The result is shown by Table 4.7
80
Table 4.7 Work_or_Not * Parking_duration Crosstabulation
Parking_duratoin Total
1 or less 2 3 4 5
Work_or_Not
Non-work
Count 26 47 29 13 52 167
Expected
Count 28.0 44.4 28.7 12.9 53.0 167.0
Work
Count 13 15 11 5 22 66
Expected
Count 11.0 17.6 11.3 5.1 21.0 66.0
Total
Count 39 62 40 18 74 233
Expected
Count 39.0 62.0 40.0 18.0 74.0 233.0
Insignificant association. Chi-square test = 1.091 (df =4). P= 0.896
The p-value is very high (0.896) in this case, which illustrates that there is definitely
no association between parking durations and travel purposes of parking users in the
context of Cardiff city centre. The hypothesis is null and no difference in parking
durations exists between individuals who visit Cardiff for work or non-work
purposes.
4.3.6 Conclusions of relations in parking users’ profiles
Through proposing hypothesis and the relative chi-square tests, several noticeable
findings in terms of interactions across parking users’ travelling characteristics are
obtained.
First of all, people who visit Cardiff city centre for work reasons are more likely to
travel alone, whereas individuals coming for shopping or leisure purposes tend to
bring companions with them (p-value equals 0.000). Meanwhile, people travelling
for work generally park more frequently than visitors for non-work reasons (p-value
equals 0.000). Finally, female parking users are more likely to travel for shopping or
leisure in Cardiff city centre, while male parking users tend to travel for working or
business reasons (p-value equals 0.013).
81
In addition, from null hypotheses, it can be argued that people in different age
groups generally do not have differences in travel purposes to Cardiff city centre
(p-value equals 0.280). Meanwhile, the parking durations of short-stay parking users
do not tend to vary against variations in travel reasons (p-value equals 0.896).
4.4 Logistic Regression
From previous studies, it can be concluded that individuals’ parking choice behaviour
is influenced by various factors such as parking charge, parking availability, distance
to destination, safety and parking users’ characteristics, etc. (Hunt and Teply 1992;
Teknomo and Hokao 1997; Hensher and King 2001;Hess and Polak 2004). In this
section, the study will develop logistic regression models to obtain parking users’
sensitivities to the most policy-related parking features: parking pricing and
availability (time to find a parking space) in the context of Cardiff city centre. The
core data used are from the discrete parking choices questions of the questionnaire
(B1 to B4). Meanwhile, other data related to parking users’ basic profiles will also be
used to test the ‘taste variations’ to parking features across various travel groups.
4.4.1 Choosing frequencies of alternatives
The main survey has obtained 233 completed questionnaires and each questionnaire
has four discrete choice questions. Thus, the study has a total of 932 sets of
hypothetical parking choice data. Meanwhile, the study provides four alternatives
for respondents: (1) Continue to park here,(2) Park elsewhere, (3) Travel by other
modes and (4) Not make the trip. The following Figure 4.19 describes the
percentages of respondents’ choosing frequencies for these four alternatives.
82
It is obvious that the alternatives ‘continue to park here’ and ‘park elsewhere’ have
been chosen most frequently with the percentages of 45.5% and 43.6% separately.
However, the other two alternatives take a much smaller account. Based on the fact
that Alternative 3 and 4 are rarely chosen (9.8% and 1.2% separately) by the
respondents, it is reasonable to combine them with Alternative 2 to form the new
category: ‘Not continue to park here’. Therefore, the options provided can be
classified into two categories: ‘Continue to park here’ (Alternative 1) and ‘Not
continue to park here’ (Alternatives 2, 3 and 4). Meanwhile, descriptive statistics can
be conducted for respondents who have chosen ‘Not continue to park here’. It is
found that most parking users (79.9%) will search for another parking place if they
are not satisfied with the parking charge and availability of the current place. 17.9%
of the respondents will transfer to other transport modes such as bus, train and
cycling. Only a few, 2.2%, will not make the trip (Figure 4.20).
45.50%
43.60%
9.80%
1.20%
Figure 4.19 Choosing frequencies of alternatives
1.Continue to park here
2. Park elsewhere
3.Travel by other modes
4. Not make the trip
83
Moreover, the survey is not intended and designed to acquire the data to identify
the factors which can influence parking users’ trade-off among Alternatives 2,3 and 4
when they choose not to continue to park at the current location. Hence, in terms of
the logistic regression, this combination is more practical and can assure the
accuracy of the results. In SPSS, people’s choices will be coded into the variable
‘Park_or_not’ where 1 represents the choice of ‘continue to park here’ (Alternative 1)
and 0 stands for choosing ‘not continue to park here’(Alternatives 2, 3 and 4).
4.4.2 Independent samples t-test
A t-test should be conducted prior to the regression to examine the validity of the
collected data. As with the increase in parking charge and decline in parking
availability, the probability of a parking user choosing to continue to park will
decrease (Feeney 1988; Young et al.1991). Therefore, compared with people who
choose ‘continue to park here’, respondents who choose ‘not continue to park here’
should have higher mean values in terms of the two attributes: ‘Increase in parking
price’ and ‘Time to find a parking space’. Otherwise, the validity of the collected data
will be dubious. Therefore, the t-test is necessary to be conducted to identify
whether there are significant differences in the mean values of these two groups.
79.9%
17.9%
2.2%
Figure 4.20 Percentages of parking users' choices if they choose not to park at the current location
Park elsewhere
Travel by other modes
Not make the trip
84
The dependent variables for the t-test are ‘increase in parking price’ (variable
‘INCR_COST’ in SPSS) and ‘time to find the parking space’ (variable ‘TIME’ in SPSS).
And the independent variables are people choices between ‘park’ and ‘not park’.
Table 4.8 Independent samples t-test result
Park_or_not N Mean Std. Deviation Std. Error Mean
INCR_COST Continue to park here 424 1.283 .7572 .0368
Not continue to park here 508 2.137 .7144 .0317
TIME Continue to park here 424 2.55 2.167 .105
Not continue to park here 508 3.38 2.209 .098
INCR_COST: Significant difference. Sig.(2-tailed): 0.000. Mean difference: -0.8538
TIME: Significant difference. Sig.(2-tailed): 0.000. Mean difference: -0.830
It can be seen from the t-test result that significant differences exist in the mean
values of INCR_COST and TIME (both Sigs=0.000) between two the choice groups.
The mean value of ‘INCR_COST’ of people who choose ‘continue to park’ is about
£0.85 lower than people who choose ‘not continue to park’. Similarly, the mean
value of ‘TIME’ of the former is also 0.83 minutes lower than the later. Hence, this
result in the context of Cardiff city centre corresponds to the findings of previous
studies and the collected data is proved to be valid.
4.4.3 Modelling parking users’ general sensitivity to parking features
Based on the previous studies, with regard to the context of Cardiff city centre, it can
be hypothesised that increases in parking pricing (INCR_COST) and time to find
parking place (TIME) can adversely influence individuals’ probability to continue to
park at the current location. Binary logistic regression will be developed to test this
hypothesis and obtain parking users’ general sensitivities to these two parking
features.
85
The regression equation is:
Y: the log odds of choosing ‘continue to park here’ (compared with the odds of ‘not
continue to park’). Y= [
]
α: the constant of the equation
β1= the coefficient of variable INCR_COST
INCR_COST= Increase in parking charge
β2= the coefficient of variable TIME
TIME= Time to find a parking space
The regression result is shown by Table 4.9 (Full regression result is listed in
Appendix III)
Table 4.9 Binary regression result for general parking users
B S.E. Wald df Sig. Exp(B)
INCR_COST -1.492 .106 196.302 1 .000 .225
TIME -.226 .036 39.159 1 .000 .797
Constant 3.034 .239 161.198 1 .000 20.789
The significant coefficients of INCR_COST and TIME are both 0.000 (less than 0.05),
which has illustrated that increase in parking charge and searching time have an
important influence on peoples’ parking choice behaviour.
From the modelling result, the regression equation should be:
If searching time is controlled at a certain degree, one unit (£1) increase in parking
charge will decrease the log odds of continuing to park by 1.492 (the probability will
be times as much as the previous probability). Similarly, 1 minute
86
increase in searching time will decrease the log odds by 0.226 (the possibility will be
0.797 times as much as the previous one).
4.4.4. Modelling parking users’ taste variations to parking features
The above model has obtained parking users’ general sensitivities to changes in
parking features. Whereas, different user groups usually tend to have variations in
sensitivities (taste variation). Hess and Polak (2004) have applied a MMNL model to
acquire the taste variations across parking users. But this thesis will try to use
another method to obtain this taste variation in the context of Cardiff city centre.
The study assumed that parking users with different personal characteristics (gender
and age) and trip characteristics (travel purpose and travel group size) might have
different sensitivities to the changes in parking pricing and availability. Binary logistic
models will be adopted to test the assumptions.
Gender:
The regression equation in this case should be:
: Variable INCR_COST times the dummy value ‘Female’
: Variable TIME times the dummy value ‘Female’
Compared with the equation to model the general sensitivity, two new variables are
added into the new equation: and . ‘Female’
is a dummy variable in the equation. If a respondent’s gender is female, the variable
‘Female’ will equal 1.Otherwise it will be 0 (male as the reference). If the resulted
coefficient or is significant (sig<0.05), then it can be argued that compared to
the males, female parking users have different sensitivities to parking features.
The binary regression result is shown in Table 4.10.
87
Table 4.10 Binary regression result for parking users with different genders
B S.E. Wald df Sig. Exp(B)
INCR_COST -1.473 .123 144.562 1 .000 .229
TIME -.203 .047 18.382 1 .000 .816
INCR_COST Female -.034 .115 .088 1 .766 .966
TIME Female -.043 .058 .564 1 .453 .958
Constant 3.036 .239 161.356 1 .000 20.814
The significances of ‘INCR_COST∙Female’ and ‘TIME∙Female’ are 0.766 and 0.453
separately. They are both much larger than the threshold value 0.05. Thus, there is
no sensitivity difference found between male and female parking users.
Travel Purpose:
The regression equation to test taste variations across travel purposes should be:
‘Work’ is the dummy variable in this equation. It equals 1 if a parking user travels for
work reasons and equals 0 if an individual comes for shopping or leisure (references).
To be noticed, respondents come for reasons such as a ceremony or an examination
are also coded to ‘Work’ since they also have to reach a specific location at a certain
time like other working people. The test result is shown by Table 4.11.
Table 4.11 Binary regression result for parking users with different travel
purposes
B S.E. Wald df Sig. Exp(B)
INCR_COST -1.530 .121 161.048 1 .000 .217
TIME -.285 .045 39.288 1 .000 .752
INCR_COST∙Work .042 .118 .127 1 .722 1.043
TIME∙Work .121 .059 4.247 1 .039 1.129
Constant 3.074 .241 162.725 1 .000 21.624
88
From the result, no significant difference (Sig of ‘INCR_COST∙Work’ equals 0.722) is
found between people who travel for work and non-work reasons in terms of
sensitivity to parking charges. However, people coming for work purposes are less
sensitive to the changes in searching time compared to people coming for non-work
reasons ( equals 0.121, sig =0.039). Under a unit change in searching time for
parking places, the log odds of continuing to park of working people is 0.121 larger
than that of non-working people (the possibility is 1.129 times as much as that of
non-work people). This finding is different to the common notion that parking users
with work purposes might be more sensitive to the searching time for parking spaces.
This can be explained as being that people travel for work reasons are less free in the
choice of parking places since they usually have to work at a certain location.
Meanwhile, since working people usually arrive relatively earlier than other parking
users, in practice the searching process would be not difficult for them. Therefore,
they tend to be less sensitive to searching time for parking spaces.
The resulted equation should be:
Age group:
Respondents’ age are classified into three categories for developing the model:
17-24 (reference), 25-45 (Variable ‘Age25_44’) and over 45 (Variable ‘Ageover45’ ).
The regression equation should be:
over45 + over45
In this case, ‘Age25_44’ and ‘Ageover45’ are dummy variables and respondents who
aged from 17- 24 are the reference. The regression result is shown in Table 4.12.
89
Table 4.12 Binary regression result for parking users belonging to different age
groups
B S.E. Wald df Sig. Exp(B)
INCR_COST -1.212 .172 49.616 1 .000 .298
TIME -.276 .077 12.716 1 .000 .759
INCR_COST∙Over45 -.144 .175 .680 1 .409 .866
TIME∙Over45 .020 .088 .050 1 .824 1.020
TIME∙Age25_44 .084 .085 .978 1 .323 1.088
INCR_COST∙Age25_44 -.489 .171 8.131 1 .004 .613
Constant 3.059 .241 160.858 1 .000 21.309
It is obvious that parking users aged from 25-44 are more sensitive to changes in
parking price (sig=0.004). Compared with the individuals aged from 17-24, their
general log odds of continuing to park here will be 0.489 lower (the possibility will be
0.613 times as much as that of people aged 17-24) against a £1 increase in parking
charge. Meanwhile, no sensitivity difference has been found with regard to parking
users aged over 45 (Sigs>0.05). The equation in this case is:
Travel group:
Finally, whether the size of travel group can influence the parking probability will be
tested. Based on the survey data about travel group size, a new dummy variable
‘Companion’ is created in SPSS. In ‘Companion’, 1 stands for people who travel with
companions while 0 stands for people who travel alone (reference). The form of the
regression equation should be:
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Table 4.13 Binary regression result for parking users with different travel group
sizes.
B S.E. Wald df Sig. Exp(B)
INCR_COST -1.648 .132 156.405 1 .000 .192
TIME -.201 .048 17.138 1 .000 .818
INCR_COST∙Companion .251 .119 4.436 1 .035 1.286
TIME∙Companion -.047 .058 .650 1 .420 .954
Constant 3.057 .241 161.436 1 .000 21.268
According to the result, parking users who travel with companions are less sensitive
to the increase in parking price (sig=0.035). Against one unit (£1) increase in parking
charge, their log odds of continuing to park at the current location is 0.251 larger
than that of lone travellers (the possibility is 1.286 times as much as that of lone
travellers). Meanwhile, different travel sizes tend not to differ in the sensitivity to
searching time for parking spaces (sig=0.420). Thus, the finalised equation should be:
The study has by now developed logistic models for different parking user groups.
The main findings are listed in the following table.
Table 4.14 Summarisation of parking user groups’ different sensitivities to parking
features
Note: [+] means less sensitivity to a parking feature; [-] means more sensitivity to a parking
feature; [N/A] means no sensitivity difference exists for a specific variable.
Classification
standards of parking
user groups
Variables(Categories) sensitivity
to parking
charge
sensitivity to parking
availability
(searching time)
Gender Female (Dummy)
Male (Reference)
[N/A]
[N/A]
[N/A]
[N/A]
91
Travel purpose Work (Dummy)
Non-work (Reference)
[N/A]
[N/A]
[+]
[-]
Age group 25-44(Dummy)
Over 45(Dummy)
17-24(Reference)
[-]
[N/A]
[+]
[N/A]
[N/A]
[N/A]
Travel Group Size Companion(Dummy)
Travel
alone(Reference)
[+]
[-]
[N/A]
[N/A]
4.4.5 Modelling sensitivities of parking users with various characteristics
The above chapter has classified parking users into different categories through
various standards: age, gender, travel purpose and travel group size. Varied
sensitivities to parking price and availability have been identified across these
categories. However, there is a limitation of the above binary logistic models in that
each of them only takes one characteristic into account for the prediction. In almost
all cases, the profile of a parking user is the combination of various characteristics.
The above models cannot predict the parking possibility against the mutual impact
of these characteristics. For example, they cannot answer the question that what is
the parking possibility of a 30-year-old male parking user who travels alone to Cardiff
city centre for work purpose against the changes in parking features? The following
model will consider all the possible influential factors on sensitivities to parking
features simultaneously and try to predict the parking possibility of parking users
with various characteristics. Thus, the regression equation should be:
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The following Tables 4.14 and 4.15 show the result of the variables in the equation.
The full regression result can be seen in Appendix IV.
Table 4.15 Binary regression result for parking users with various characteristics
(step 1)
B S.E. Wald df Sig. Exp(B)
INCR_COST -1.404 .226 38.461 1 .000 .246
TIME -.333 .104 10.263 1 .001 .717
INCR_COST∙Female -.096 .123 .619 1 .432 .908
TIME∙Female -.018 .061 .083 1 .774 .983
INCR_COST∙Work .066 .125 .281 1 .596 1.068
TIME∙Work .124 .062 3.992 1 .046 1.132
INCR_COST∙Age25_44 -.459 .175 6.857 1 .009 .632
TIME∙Age25_44 .105 .088 1.414 1 .234 1.110
INCR_COST∙Over45 -.131 .178 .539 1 .463 .877
TIME∙Over45 .019 .091 .045 1 .832 1.019
INCR_COST∙Companion .274 .126 4.700 1 .030 1.315
TIMECompanion -.006 .062 .010 1 .921 .994
Constant 3.132 .245 163.249 1 .000 22.925
93
Variables with insignificant coefficients (sig>0.05) do not tend to influence the
probability of parking and should be removed from the model. Meanwhile,
significant variables will be taken into account for Step 2 of the modelling. The
remained variables for Step 2 are: ‘INCR_COST’, ‘TIME’, ‘Time∙Work’,
‘INCR_COST∙Age25_44’ and ‘INCR_COST∙Companion’.
Table 4.16 Binary regression result for parking users with various characteristics
(step 2)
B S.E. Wald df Sig. Exp(B)
INCR_COST -1.555 .132 139.152 1 .000 .211
TIME -.297 .043 48.074 1 .000 .743
TIME∙Work .145 .045 10.562 1 .001 1.156
INCR_COST∙Age25_44 -.248 .088 8.001 1 .005 .780
INCR_COST∙Companion .239 .091 6.948 1 .008 1.270
Constant 3.132 .245 163.895 1 .000 22.915
Based on the result, the finalised equation should be:
The interpretation of the equation can be: As the increase in parking charge and
searching time for a parking space, parking users will be less likely to continue to
park at the current location. Meanwhile, people who travel to Cardiff city centre for
work purposes are less sensitive to the increase in searching time than the others
who come for shopping or leisure. Parking users aged from 25-44 are more sensitive
to the increase in parking price. Finally, people who travel with companions tend to
be less sensitive to the parking charge. Theoretically, this model can be applied to
94
obtain the sensitivities to parking features for every individual with unique travel
characteristics. For example, the equation to predict the parking possibility of a 30-
year-old male parking user who travels alone to Cardiff city centre for work purpose
should be:
= 3.132-1.803 INCR_COST - 0.152 TIME
A one-unit (£1) increase in parking charge will decrease his log odds of continuing to
park by 1.803. Meanwhile, a one-minute increase in searching time will cause the log
odds to decline by 0.152.
4.5 Conclusion of the data analysis
Through the analysis of the survey data, the study has obtained important findings.
First of all, parking users’ profiles in the Cardiff context is acquired. It can help
transport planners better understand the question of who the parking users in
Cardiff city centre.
With regard to perceptions to parking service, parking users are generally satisfied
with the parking service provided by Cardiff city centre (the average rating is 3.97/5).
However, several issues have been identified from their responses. For example, the
guidance information on the pay machine is confusing and can cause inconvenience
to unfamiliar parking users. 15% of the respondents think the short-stay parking
charge in Cardiff city centre is still too expensive to them. Meanwhile, the
overcrowded parking places in the late-time period have also caused parking
difficulties to about 10% of parking users.
The study has also identified several important relations underlying parking users’
profiles. It is found that parking users who travel for work purposes tend to drive
alone and park more frequently compared with the others who come for non-work
95
reasons. Meanwhile, female parking users are more likely travel to Cardiff city centre
for shopping or leisure than male parking users.
Finally, the study develops binary regression models to test parking users’
sensitivities to the changes in parking charge and availability. For general parking
users, a £1 increase in parking charge will cause the log odds of continuing to park to
decline by 1.492. A one-minute increase in searching time will decrease the log odds
by 0.226. Meanwhile, the study has also obtained the taste variations across
different parking user groups. It has found that characteristics such as age, travel
purpose and travel group size can lead to significant differences in parking users’
sensitivities to parking features.
96
5. Conclusions and Recommendations
Based on a parking user survey in the main short-stay parking places around Cardiff
city centre, the thesis has provided a thorough analysis of individuals’ parking choice
behaviour. Through developing statistical tools, various important discoveries have
been achieved. The findings of this thesis will directly contribute to the parking
policy improvement in Cardiff.
In Chapter 2, a critical literature review has been demonstrated. According to the
previous studies, individuals’ parking choice behaviour is influenced by many factors.
Parking pricing, parking availability, walking distance to destination, ease of
ingress/egress and even personal characteristics can affect motorists’ parking
decisions (Young 1986; Miller 1993; Waerden et al. 2003). Across the various factors,
parking charge and parking availability are identified as the features which are most
related to parking policy. Transport planners can use parking pricing and parking
supply management to achieve travel demand control for urban areas (Feeney 1988;
Calthrop et al. 2000). As a study that is aimed at contributing to parking policy
improvement, the thesis decides mainly to explore the influences of these two
parking features on people’s parking behaviour. The necessity of applying parking
pricing to ease congestion and relative externalities in city centres has been
demonstrated by previous studies (Clinch and Kelly 2003; Shoup 2011). However, the
pricing scale is found to be hard to determine based on the studies so far.
Meanwhile, another important parking policy tool, parking availability management
is shown to cause more serious congestion issues to urban areas if applied alone. An
efficient parking policy should implement these two policy tools simultaneously
(Anderson and Palma 2004; Shoup 2006). The thesis has tried to seek the balance
between applying parking charge and parking supply management from a new angle:
parking users’ sensitivity. Through acquiring people’s sensitivities to parking features,
variations in parking possibilities against unit changes in parking pricing or parking
availability can be predicted. Thus, the scales of parking pricing and parking spaces
97
supply control can be determined, based on transport planners’ anticipations.
Meanwhile, discrete choice models should be developed to obtain parking users’
sensitivities. Several previous studies have successfully modelled individuals’ parking
choice behaviour in the context of different regions in the world. However, no
specific modelling has been developed to study the parking choice behaviour in the
Cardiff background up to now. This thesis has decided to fulfil this gap. Across the
various types of discrete choice models, the Mixed Multinomial Logit (MMNL) model
can provide more accurate predictions since it can obtain the taste variations across
parking users’ characteristics (Hess and Polak 2004). The thesis has tried to
innovatively achieve the similar function of MMNL model using standard binary
logistic models. Compared with MMNL, the developed models in this study are more
simplified. Taste variations across parking users are captured with the help of the
data set from background questions in the survey. The innovation of mixing
background data into stated-preference based modelling has largely improved the
prediction accuracy of standard discrete choice models and can provide references
for future studies.
In Chapter 3, the conceptualisation process of the study is introduced. Through the
literature review and the suggestions from the parking experts at Cardiff Council and
the British Parking Association, the objectives of the study have been identified
explicitly. The targeted questionnaire for the survey has been designed based on the
determined research objectives. It mainly contains three sections: (1) Background
questions in terms of parking users’ personal and travel characteristics. (2) Rating
questions to acquire respondents’ levels of satisfaction with the parking service in
Cardiff city centre. (3) Stated-preference discrete choice questions to observe
individuals’ parking choices against various combinations of parking features. A prior
pilot survey has also been conducted to test the rationality of the questionnaire
design and determine the survey time and locations for the main survey. Using
98
random sample method, the main survey of this research has successfully obtained
233 respondents in the main short-stay parking places around Cardiff city centre.
In the core data analysis chapter, descriptive statistics are initially applied to acquire
the basic profiles of parking users. The parking users in Cardiff city centre are mostly
from the locality (78.97%) and mainly travel to the city centre from home (87.6%)
rather than work or other places (12.4%). Meanwhile, 58.4% of the parking users
travel without adult companions and 79.8% travel without child companions. In
terms of travel purpose, 55.3% of parking users travel to Cardiff city centre for
shopping or leisure; 28.3% travel for work reasons and 16.3% come for other specific
reasons such as a graduation ceremony or an appointment. Short-stay parking users’
average parking duration is 3.10 hours.27.5% of them park at the specific short-stay
parking location at least once a week, whereas the other 72.5% tend to park less
frequently. On average, parking users have to spend 1.48 minutes searching time for
available parking spaces and the mean distance from the car park to their trip
destinations is 4.75 minutes on foot. The main reason of respondents for choosing
the specific parking place is ‘close to destination’ (68.7%), while the most overlooked
parking feature is parking safety (2.1%). Meanwhile, during the survey, only 5.6%
respondents have stated that they have heard of the ‘Park Mark’ of the BPA which
illustrates that the popularity of this safety scheme in short-stay parking spaces is
not satisfying in the Cardiff city centre background. Details of the statistics of parking
users’ profiles are contained in Chapter 4.1.
Parking users are generally satisfied with the parking service provided in Cardiff with
an average rating of 3.97/5 across all parking features. However, several underlying
issues have been found during the analysis. 15% of respondents complain that the
current parking tariff is still expensive. Meanwhile, about 10.7% of parking users are
not satisfied with the parking availability in Cardiff city centre. Finding a parking
space will be quite hard for motorists who arrive at late time periods. In addition,
99
many respondents have complained that the guidance information on parking pay
machines is confusing and inconvenient. Thus, the thesis recommends that this
information be revised and made into a more straightforward version. Although the
pay machines have the device to support card payment, it is observed during the
survey that the function does not always work in practice. Thus, it is also suggested
that more frequent maintaining of the machines be conducted to ensure the ranges
of payment options. Moreover, although more than 90% of parking users are
satisfied with the parking safety, some still show concern that the parking spaces are
too small to avoid rubs between vehicles. Solving the above issues can directly help
to improve the parking service and encourage individuals’ compliance to the parking
policy in Cardiff city centre. The analysis details of parking users’ perceptions can be
seen in Chapter 4.2.
Chi-square tests have also been conducted to explore the underlying relations across
parking users’ profiles. It is found that, in the context of Cardiff city centre, people
who travel for work purposes tend to park more frequently compared with people
who come for shopping or leisure (p-value=0.000). Meanwhile, parking users for
non-work reasons are more likely to bring travel companions with them, while the
others who come for work purposes tend to travel alone (p-value=0.000). Also,
differences in travel purposes have been identified between different genders.
Female parking users are more likely to travel for shopping or leisure, whereas male
parking users are more possibly coming for work reasons (p-value =0.013). Details of
the chi-square test results have been illustrated in Chapter 4.3.
The validity of the collected stated-preference data has first of all been confirmed
through the independent sample t-test. Discrete models have subsequently been
developed to simulate the impacts of parking features on individuals’ parking choice
behaviour. With regard to parking users’ general sensitivities to parking charges and
parking availability, the resulted regression equation is: ‘
100
’. A £1 increase in parking charge will decrease the log
odds of continuing to park at the current place by 1.492 (the parking possibility will
be times as much as the possibility before a pricing increase).
Meanwhile, a one-minute increase in searching time for parking spaces will decrease
the log odds of parking by 0.226 (the possibility will be 0.797 times as much as the
possibility before a searching time increase). Thus, the result has quantified parking
users’ reflections against variations in parking features. It can provide an important
reference to the parking policy making. For instance, if planners aim to reduce the
parking demand by about 25 percent in Cardiff city centre, they can increase the
general parking charge by 20 pence or reduce the parking availability to increase the
general searching time by 1.3 minutes (the possibility will decline by 25.8%).
Moreover, the combined changes in both the two parking features can be applied to
achieve the same goal and might be more effective because of the joint utilisation of
parking policy tools. The balance between parking pricing and supply management
can be found and examined in practice. The thesis has also modelled the taste
variations across different parking users groups. It has found that people travelling
for shopping or leisure purposes are virtually more sensitive to the increase in
searching time. This result has supported another finding under the UK context: Still
and Simmonds (1999) argue that shoppers are more sensitive to the adverse parking
conditions because they are freer in parking location choice compared to working
people. Meanwhile, parking users aged from 25-44 are more sensitive to the
changes in parking charge than individuals belonging to other age groups.
Additionally, people who travel with companions tend to be less sensitive to the
increase in parking pricing (Details in modelling can be seen in the Chapter 4.4).
Based on these sensitivity variations across parking users, more targeted parking
policies can be made if planners want to change the parking behaviour of a specific
parking user group. Finally, a model has been developed to predict individuals’
parking possibilities against the combined effect of personal and travel
101
characteristics. The resulted regression equation is:
In almost all cases, in statistics, every individual can be described as the combination
of various characteristics. In this case, every individual has their unique combination
of travel purposes, age and travel group size. Therefore, this result has expanded the
model’s function to predicting the parking possibility for each individual. The model
can contribute to the design of more detailed and pointed parking policy making
within the Cardiff city centre context.
However, there are also several limitations in this thesis. First of all, this parking
choice behaviour study is conducted under the scope of council-managed on-street
short-stay parking. Other parking types in the Cardiff background such as off-street
private car parks, multistory parking and park-and-ride are not included in this
research. Future studies are recommended to analyse parking users’ behaviour in
the context of other parking types. Thus, a more comprehensive understanding of
parking choice behaviour under various parking types can be obtained. Secondly, in
this thesis, the influences of the two most policy related parking features (parking
pricing and availability) have been explored. However, other detailed factors such as
safety, comfortability and ease of ingress/egress, etc. can also influence individuals’
parking choice decisions. Hence, it is suggested that future studies should take more
influential factors into account in their analyses. Finally, in terms of individuals who
choose not to continue to park, this study has not acquired enough information to
identify the factors which can impact on their trade-off among parking elsewhere,
travelling by other modes and not making the trip. Hence, further studies are
recommended to obtain the relative data and fill this gap. Multinomial logit models
and nested logit models are suggested if future studies can acquire more thorough
data sets which can help classify individuals’ parking choices into more categories.
102
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Appendices
Appendix I: Questionnaire for the pilot survey (BLCK_1)
Cardiff City Centre Parking-User Survey
Hello my name is {your name} and is a student at Cardiff University.
As part of a research project, we are conducting a survey of parking users. The
survey will only take 5-7 minutes of your time. Would it possible to ask you a few
questions about parking? You can refuse to answer any of the questions that
follow. Your answers are anonymous, and only aggregate statistics will be
published. You can refuse to answers any questions you do not wish to and you can
quit at time during this survey. Thank you.
Interview start time {record start time}
Section A: Parking information
Q1. Where are you travelling from? {ask for name of place and postcode}
Locality1a(more specific than Cardiff)
______________________________
Postcode1b: _________________
Q2. Would that be home, work or other place?
1 Home 2 Work 3 Other_________________
Q3. What is the main reason for travelling to Cardiff City centre today?
1 Shopping 2 Work/Business
3 Leisure 4 Other
Q4. How long are you planning to park here?{record the time and minutes/hours}
_________________
Q5. What is the reason for choosing to you park here? {mark the most appropriate to the
answer}
1 It is the only one I
know
2 Close to destination 3 Easy to find a parking space
4 Reasonable parking
price
5 It is safe to park here 6 Other (please
specify)____________
Q6. How long did it take to find a parking space? {Check either immediately or stated time}
1 Immediately upon arrival OR record time (minutes) ________________________
110
Q7. How often do you park here?
1 Every weekday 2 2-3 times a week 3 Once a week 4 2-3 times a
month
5 Every fortnight 6 Once a month 7 This is the first time I visit Cardiff City
centre
Q8. Approximately how far (in minutes) is your end destination from this car park?
Please write here: ______________________________
Q9. How many adults/children are travelling with you today?
Adults10a (note number) ______________ Children10b (note number) ________________
Q10. Please rate from 1 to 5 how satisfied you are with the following aspects of
parking? (5 being very ‘satisfied’ and 1 being ‘very dissatisfied’ )
Aspects Rate
Parking charge
Ease of finding a parking space
Clarity of information on pay and display machines e.g. pricing, length of stay, etc.
Range of payment of options
Personal safety
Vehicle safety
Section B: Hypothetical parking choices
B1. If the cost of the fare for you to park today increased by £1.00 and you could find parking
space immediately, then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
B2. If the cost of the fare for you to park today increased by £2.00 and the time to find a park
space was 2 minutes, then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
B3. If the cost of the fare for you to park today increased by £1.50 and the time to find a park
space was 6 minutes, then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
111
B4. If the cost of the fare for you to park today increased by £2.50 and the time to find a park
space was 2 minutes, then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
Section C: Parking user information
Q11. {surveyor records this without asking} 0 Male 1 Female
Q12. What is your age? You only need to indicate in which age group you belong to.
Would you say?
117-24 225-34 335-44 445-55 555-65 6Over 65 7Refused
Thank you very much for your time!
It is very much appreciated. My department is the School of Planning and Geography in Cardiff
University. Should you have any enquire about this study, please contact my supervisor Dr
Dimitris Potoglou on tel. 02920876088; Email: [email protected]
Interview End time {record end time}
112
Appendix II: Questionnaire for the main survey (BLCK_1)
Cardiff City Centre Parking-User Survey
Hello my name is {your name} and is a student at Cardiff University.
As part of a research study, we are conducting a survey of parking users. The survey
is about 5 minutes of your time. Would it possible to ask you a few questions about
parking? Your answers are anonymous. You can refuse to answers any questions
you do not wish to and you can quit at time during this survey. Thank you.
Interview start time {record start time}
Section A: Parking information
Q1. Where are you travelling from? {ask for name of place and postcode}
Locality1a(more specific than Cardiff)
_____________________________
Postcode1b:_____________________
Q2. Would that be home, work or other place?
1 Home 2 Work 3 Other_________________
Q3. What is the main reason for travelling to Cardiff City centre today?
1 Shopping 2 Work/Business
3 Leisure 4 Other
Q4. How long are you planning to park here? {record the time in minutes/hours}
_________________
Q5. What is the reason for choosing to park here? {mark the most appropriate to the
answer}
1 It is the only car park
I know
2 Close to destination 3 Easy to find a parking space
4 Reasonable parking
price
5 It is safe to park here 6 Other (please
specify)____________
Q6. How long did it take to find a parking space? {Check either immediately or stated time}
1 Immediately upon arrival OR record time (minutes) ________________________
113
Q7. How often do you park here?
1 Every weekday 2 2-3 times a week 3 Once a week 4 2-3 times a
month
5 Every fortnight 6 Once a month 7 This is the first time I visit Cardiff City
centre
Q8. Approximately how far (in minutes) is your end destination from this car park?
Please write here: ______________________________
Q9. Including yourself, how many adults/children are travelling with you today?
Adults10a (note number) ______________ Children10b (note number) ________________
Q10. Please rate from 1 to 5 how satisfied you are with the following aspects of
parking? (5 being very ‘satisfied’ and 1 being ‘very dissatisfied’)
Aspects Rate Reason for low rating
Parking charge
Ease of finding a parking space
Clarity of information on pay and display machines e.g. pricing,
length of stay, etc.
Range of payment of options
Personal safety
Vehicle safety
Q11. Have you heard of 'Park Mark?' 0 Yes 1 No
Section B: Hypothetical parking choices
B1. If the cost to park today increased by £1.00 and you could find parking space immediately,
then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
B2. If the cost to park today increased by £2.00 and the time to find a park space was 2 minutes,
then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
114
B3. If the cost to park today increased by £1.50 and the time to find a park space was 6 minutes,
then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
B4. If the cost to park today increased by £2.50 and the time to find a park space was 2 minutes,
then what would be your choice?
1 Continue to
park here
2 Park elsewhere 3 Travel by other mode:
______________________
4 Not make the
trip
Section C: Parking user information
Q12. {surveyor records this without asking} 0 Male 1 Female
Q13. What is your age? You only need to indicate in which age group you belong to.
Would you say?
117-24 225-34 335-44 445-55 555-65 6 Over 65 7Refused
Thank you very much for your time!
It is very much appreciated. My department is the School of Planning and Geography in Cardiff
University. Should you have any enquire about this study, please contact my supervisor Dr
Dimitris Potoglou on tel. 02920876088; Email: [email protected]
Interview End time {record end time}
115
Appendix III: Logistic regression result of parking users’ general sensitivities to
parking charge and parking availability.
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 932 100.0
Missing Cases 0 .0
Total 932 100.0
Unselected Cases 0 .0
Total 932 100.0
a. If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
Not continue to park here 0
Continue to park here 1
Block 0: Beginning Block
Classification Tablea,b
Observed Predicted
Park_or_not Percentage
Correct Not continue
to park here
Continue to
park here
Step
0
Park_or_not Not continue to park
here
508 0 100.0
Continue to park here 424 0 .0
Overall Percentage 54.5
a. Constant is included in the model.
b. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.181 .066 7.550 1 .006 .835
116
Variables not in the Equation
Score df Sig.
Step 0 Variables INCR_COST 234.435 1 .000
TIME 32.118 1 .000
Overall Statistics 264.948 2 .000
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 296.848 2 .000
Block 296.848 2 .000
Model 296.848 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 987.597a .273 .365
a. Estimation terminated at iteration number 5 because parameter estimates changed by less
than .001.
Classification Tablea
Observed Predicted
Park_or_not Percentage
Correct Not continue
to park here
Continue to
park here
Step
1
Park_or_not Not continue to park
here
389 119 76.6
Continue to park here 118 306 72.2
Overall Percentage 74.6
a. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a INCR_COST -1.492 .106 196.302 1 .000 .225
TIME -.226 .036 39.159 1 .000 .797
Constant 3.034 .239 161.198 1 .000 20.789
a. Variable(s) entered on step 1: INCR_COST, TIME.
117
Appendix IV: Logistic regression result of parking users’ sensitivities under the
combinations of various characteristics.
Step 1:
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 932 100.0
Missing Cases 0 .0
Total 932 100.0
Unselected Cases 0 .0
Total 932 100.0
a. If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
Not continue to park here 0
Continue to park here 1
Block 0: Beginning Block
Classification Tablea,b
Observed Predicted
Park_or_not Percentage
Correct Not continue
to park here
Continue to
park here
Step
0
Park_or_not Not continue to park
here
508 0 100.0
Continue to park here 424 0 .0
Overall Percentage 54.5
a. Constant is included in the model.
b. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.181 .066 7.550 1 .006 .835
118
Variables not in the Equation
Score df Sig.
Step 0 Variables INCR_COST 234.435 1 .000
TIME 32.118 1 .000
INCR_COSTFemale 54.462 1 .000
TIMEFemale 17.694 1 .000
INCR_COSTWork 14.524 1 .000
TIMEWork .238 1 .626
INCR_COSTAge25_44 78.964 1 .000
TIMEAge25_44 16.667 1 .000
INCR_COSTOver45 12.614 1 .000
TIMEOver45 3.750 1 .053
INCR_COSTCompanion 29.472 1 .000
TIMECompanion 7.825 1 .005
Overall Statistics 286.180 12 .000
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 328.128 12 .000
Block 328.128 12 .000
Model 328.128 12 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 956.317a .297 .397
a. Estimation terminated at iteration number 5 because parameter estimates changed by less
than .001.
119
Classification Tablea
Observed Predicted
Park_or_not Percentage
Correct Not continue
to park here
Continue to
park here
Step
1
Park_or_not Not continue to park
here
399 109 78.5
Continue to park here 117 307 72.4
Overall Percentage 75.8
a. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step
1a
INCR_COST -1.404 .226 38.461 1 .000 .246
TIME -.333 .104 10.263 1 .001 .717
INCR_COSTFemale -.096 .123 .619 1 .432 .908
TIMEFemale -.018 .061 .083 1 .774 .983
INCR_COSTWork .066 .125 .281 1 .596 1.068
TIMEWork .124 .062 3.992 1 .046 1.132
INCR_COSTAge25_44 -.459 .175 6.857 1 .009 .632
TIMEAge25_44 .105 .088 1.414 1 .234 1.110
INCR_COSTOver45 -.131 .178 .539 1 .463 .877
TIMEOver45 .019 .091 .045 1 .832 1.019
INCR_COSTCompanion .274 .126 4.700 1 .030 1.315
TIMECompanion -.006 .062 .010 1 .921 .994
Constant 3.132 .245 163.249 1 .000 22.925
a. Variable(s) entered on step 1: INCR_COST, TIME, INCR_COSTFemale, TIMEFemale,
INCR_COSTWork, TIMEWork, INCR_COSTAge25_44, TIMEAge25_44, INCR_COSTOver45,
TIMEOver45, INCR_COSTCompanion, TIMECompanion.
120
Step 2:
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 932 100.0
Missing Cases 0 .0
Total 932 100.0
Unselected Cases 0 .0
Total 932 100.0
a. If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
Not continue to park here 0
Continue to park here 1
Block 0: Beginning Block
Classification Tablea,b
Observed Predicted
Park_or_not Percentage
Correct Not continue
to park here
Continue to
park here
Step
0
Park_or_not Not continue to park
here
508 0 100.0
Continue to park here 424 0 .0
Overall Percentage 54.5
a. Constant is included in the model.
b. The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.181 .066 7.550 1 .006 .835
121
Variables not in the Equation
Score df Sig.
Step 0 Variables INCR_COST 234.435 1 .000
TIME 32.118 1 .000
TIMEWork .238 1 .626
INCR_COSTAge25_44 78.964 1 .000
INCR_COSTCompanion 29.472 1 .000
Overall Statistics 282.449 5 .000
Block 1: Method = Enter
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 322.990 5 .000
Block 322.990 5 .000
Model 322.990 5 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 961.455a .293 .392
a. Estimation terminated at iteration number 5 because parameter estimates changed by less
than .001.
Classification Tablea
Observed Predicted
Park_or_not Percentage
Correct Not continue
to park here
Continue to
park here
Step
1
Park_or_not Not continue to park
here
393 115 77.4
Continue to park here 117 307 72.4
Overall Percentage 75.1
a. The cut value is .500
122
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step
1a
INCR_COST -1.555 .132 139.152 1 .000 .211
TIME -.297 .043 48.074 1 .000 .743
TIMEWork .145 .045 10.562 1 .001 1.156
INCR_COSTAge25_44 -.248 .088 8.001 1 .005 .780
INCR_COSTCompanion .239 .091 6.948 1 .008 1.270
Constant 3.132 .245 163.895 1 .000 22.915
a. Variable(s) entered on step 1: INCR_COST, TIME, TIMEWork, INCR_COSTAge25_44,
INCR_COSTCompanion.