Development of Selective Parking Discovery Algorithm for ... Qamar Thesis.pdf · Development of...
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1 Development of Selective Parking Discovery Algorithm for Parking Guidance Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Shahab Qamar BSc. (Computer Information Systems) UET Peshawar MS (Digital Design) Griffith University School of Civil Engineering and Built Environment Faculty of Science and Engineering Queensland University of Technology Australia 2019
Transcript of Development of Selective Parking Discovery Algorithm for ... Qamar Thesis.pdf · Development of...
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Development of Selective Parking Discovery Algorithm
for Parking Guidance
Submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy
Shahab Qamar
BSc. (Computer Information Systems) UET Peshawar
MS (Digital Design) Griffith University
School of Civil Engineering and Built Environment
Faculty of Science and Engineering
Queensland University of Technology
Australia
2019
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TABLE OF CONTENTS
ACKNOWLEDGMENTS ............................................................................................. 6
STATEMENT OF ORIGINAL AUTHORSHIP ........................................................... 7
ABBREVIATIONS ....................................................................................................... 8
LIST OF FIGURES ....................................................................................................... 9
LIST OF TABLES ....................................................................................................... 12
ABSTRACT ................................................................................................................. 14
CHAPTER 1. INTRODUCTION ................................................................................ 15
1.1 Background ................................................................................................................... 15
1.2 Research Gap ................................................................................................................ 16
1.3 Aim of research ............................................................................................................. 19
1.4 Significance of research ................................................................................................ 20
1.5 Thesis structure ............................................................................................................. 24
CHAPTER 2. LITERATURE REVIEW ..................................................................... 27
2.1 The parking problem and its effects .............................................................................. 28
2.1.1 Impact of Cruising ................................................................................................. 29
2.1.2 Impact on Road Capacity ....................................................................................... 30
2.1.3 Impact on Road Safety ........................................................................................... 30
2.2 Role of parking policies in parking problem ................................................................. 31
2.2.1 Effectiveness of parking policies ........................................................................... 31
2.2.2 Challenges in parking policy design ...................................................................... 32
2.2.3 Parking Policy Outcomes: Expectation vs. Reality ............................................... 33
2.3 Driver Behaviour ........................................................................................................... 34
2.3.1 The Parking Search Spiral ..................................................................................... 34
2.3.2 Choice between On-street and Off-street Parking ................................................. 37
2.3.3 To walk or not to walk? ......................................................................................... 38
2.4 Parking Choice Modelling ............................................................................................ 39
2.4.1 Parking Search Models .......................................................................................... 39
2.4.2 Challenges in Modelling Parking Search Behaviour ............................................. 41
2.5 Parking Guidance Systems ............................................................................................ 41
2.5.1 VMS based Passive Guidance ............................................................................... 43
How effective is VMS based PGS? ............................................................................................44Limitations of VMS based PGS ..................................................................................................45
2.5.2 Active Guidance .................................................................................................... 46
Effectiveness Studies ..................................................................................................................49
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Limitations of active PGS ...........................................................................................................51
2.5.3 Impact on Parking Search with Pre-Trip Information ........................................... 52
2.6 Summary ....................................................................................................................... 53
CHAPTER 3. SELECTIVE PARKING DISCOVERY ALGORITHM DESIGN ...... 57
3.1 Background ................................................................................................................... 57
3.2 Introduction to Selective Discovery Hypothesis ........................................................... 58
3.2.1 Parking occupancy forecasting ..........................................................................................593.2.2 Parking information processing based on stated-preference approach .............................61
3.3 Selective Discovery: A Queueing Theory Perspective ................................................. 62
3.3.1 Application of QT models to parking .................................................................... 63
3.3.2 QT Parking model simulation and results .............................................................. 65
3.3.2.1 Scenario t = te (High Flux or Equilibrium) .....................................................................663.3.2.2 Scenario: t > te (Low Flux) .............................................................................................713.3.2.3 Scenario: t >> te (Off-peak hour) ....................................................................................723.3.2.4 Scenario: t < te (Peak hour) .............................................................................................743.3.2.5 Scenario: t << te (Over-saturation) .................................................................................76
3.4 Development of Selective Parking Discovery Algorithm Logic................................... 79
3.4.1 Parking Occupancy Forecasting Methodology ...................................................... 79
I. Identification of Goals .......................................................................................................79II. Data Collection ..................................................................................................................80III. Data Validation .............................................................................................................80IV. Preliminary Analysis ....................................................................................................81V. Attribute Selection & Correlation .....................................................................................81VI. Forecast Modelling .......................................................................................................82
3.4.2 Information Processing Using Selective Discovery Algorithm ............................. 82
3.4.3 Central Information Processor (CIP) Design ......................................................... 85
3.5 Simulation Design ......................................................................................................... 88
3.5.1 Baseline Scenarios: ................................................................................................ 92
3.5.2 Parking Scenarios: ................................................................................................. 93
3.6 Summary ....................................................................................................................... 93
CHAPTER 4. FORECASTING CASE STUDY & SIMULATION .......................... 95
4.1 Parking Occupancy Forecasting Case Study ................................................................. 95
4.1.1 Data Preparation & Development of Analytical Tools .......................................... 96
4.1.2 Statistical Evaluation of Seasonality in Parking Occupancy ................................. 99
4.1.2.1 Seasonality detection by exploratory analysis................................................................994.1.2.2 Seasonality Detection Using FFT ................................................................................ 1004.1.2.3 Seasonality detection using ACF and PACF ............................................................... 1024.1.2.4 Verification of seasonal component using TBATS model ......................................... 103
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4.1.3 Forecasting Parking Occupancy .......................................................................... 104
4.1.3.1 Data Preparation Feature Engineering ......................................................................... 1044.1.3.2 Experiment Setup ......................................................................................................... 106
4.1.4 Forecasting Experiment Results .......................................................................... 109
4.1.4.1 Results of Boosted Decision Tree Regression Algorithm .......................................... 1094.1.4.2 Results of Neural Net Regression Algorithm .............................................................. 111
4.1.5 Conclusion ........................................................................................................... 113
4.2 SPDA Simulation and Results .................................................................................... 114
4.2.1 System Architecture ............................................................................................. 114
4.2.2 Baseline simulation scenarios .............................................................................. 117
4.2.2.1 Parking availability ...................................................................................................... 1174.2.2.2 Parking demand ............................................................................................................ 1194.2.2.3 Traffic volume .............................................................................................................. 120
4.2.3 Simulation Results ............................................................................................... 121
4.2.3.1 Scenario: Low Availability, High Fluctuation, High Demand, High Volume (LA, HF, HD, HV) ................................................................................................................................... 1214.2.3.2 Scenario: High Availability, High Fluctuation, Low Demand, Low Volume (HA, HF, LD, LV) .................................................................................................................................... 1234.2.3.3 Scenario: Medium Availability, Low Fluctuation, High Demand, Medium Volume (MA, LF, HD, MV) .................................................................................................................. 1254.2.3.4 Scenario: Prediction vs. No prediction ........................................................................ 1264.2.3.5 Scenario: Unreliability in systems inputs .................................................................... 1274.2.3.6 Comparison with OAPS ............................................................................................... 1294.2.3.7 Parking lot utilisation comparison ............................................................................... 130
4.3 Summary ..................................................................................................................... 132
CHAPTER 5. SUMMARY AND CONCLUSIONS ................................................. 134
5.1 Purpose of this study ................................................................................................... 134
5.2 The impact of parking problem, policy design and driver behaviour ......................... 136
5.3 Current parking guidance systems, their efficiency and limitations ........................... 138
5.4 Selective parking discovery algorithm and simulation findings ................................. 138
5.5 Parking occupancy forecasting methodology and case study findings ....................... 139
5.6 Implications of research .............................................................................................. 141
5.7 Limitations & recommendations for future research .................................................. 141
5.8 Conclusion .................................................................................................................. 143
REFERENCES .......................................................................................................... 144
APPENDIX ................................................................................................................ 152
A1. Coursework ................................................................................................................ 152
A2. Research Ethics / Statement ....................................................................................... 152
A3. Research Data ............................................................................................................. 152
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A4. Detailed figures .......................................................................................................... 153
A5. Results of Additional Forecasting Experiments ......................................................... 154
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ACKNOWLEDGMENTS
Foremost, I would like to thank my supervisor Dr. Marc Miska for putting in his
immense support, knowledge and enthusiasm into this study. I could not have imagined
a better supervisor.
I would also like to thank my mentoring supervisor Prof. Edward Chung for helping
me fine tune this study with his valuable insight and feedback.
Last, but not the least, I would like to thank my wife Dr. Zainab Wajih and my parents
for their continuous support throughout the course of this study.
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STATEMENT OF ORIGINAL AUTHORSHIP
The work contained in this thesis has not been previously submitted to meet
requirements for an award at this or any other higher education institution. To the best
of my knowledge and belief, the thesis contains no material previously published or
written by another person except where due reference is made.
Signature
QUT Verified Signature
Date: 2nd July 2019
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ABBREVIATIONS
API Application-programming interface
BART Bay Area Rapid Transit
CAPS Centrally assisted parking search
CIP Central Information Processor
CPZ Controlled parking zone
CSV Comma separated value
GIS Geographic information system
IDE Integrated development environment
JEC Journey evaluation controller
MAE Mean Absolute Error
MSE Mean Squared Error
NAPS Non-assisted parking search
OAPS Opportunistically assisted parking search
PDV Parking Data Visualizer
PFI Permutation Feature Importance
PGIS Parking guidance information systems
PGS Parking Guidance Systems
QT Queuing theory
RAC Royal Automobile Club
RESTful Representational State Transfer
RMSE Root Mean Squared Error
SPDA Selective parking discovery algorithm
SQL Structured Query Language
TCRP Transit Cooperative Research Program
TMH Tune Model Hyperparameters
TRAM Traffic restraint analysis Model
VMS Variable message signs
VOT Value of time
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LIST OF FIGURES Figure 1 An overview of Selective Parking Discovery ............................................................................20
Figure 2 Cities around the world that are using Parking Guidance Information Systems: San Francisco,
New York, Seattle, Los Angeles, Washington D.C., Portland, Ore., Miami, Houston, Boston,
Denver, Pittsburgh, Tampa, London, Barcelona, Paris, Tokyo, Canberra .....................................21
Figure 3 Emerging trends in parking (International Parking Institute, 2013) ..........................................22
Figure 4 Annual Smart Parking Systems Revenue by Region .................................................................23
Figure 5 Venn diagram of the multi-faceted problem of parking .............................................................27
Figure 6 Potential CO2 emissions savings through congestion mitigation on Interstate-110 in
downtown Los Angeles (Barth & Boriboonsomsin, 2009) . ...........................................................29
Figure 7 Driver preferences and the parking search spiral (Gantelet & Lefauconnier, 2006) .................35
Figure 8 Typical PGS architecture (Teng et al., 2008) .............................................................................42
Figure 9 An Example of VMS (SIEMENS, 2011) ...................................................................................44
Figure 10 PGS based on web and GIS (Liu et al., 2006) ..........................................................................46
Figure 11 Two popular apps that use OAPS. Left: Parker Mobile app by Streetline, Inc (Streetline,
2013) .................................................................................................................................................48
Figure 12 Congestion results before and after the pilot program (Xerox, 2013) .....................................51
Figure 13 Parking Destination Information Available at ParkMilwaukee.com .......................................52
Figure 14 A simplified flowchart representation of Selective Discovery ................................................59
Figure 15 Overview of proposed parking guidance system .....................................................................60
Figure 16 Parking forecast alerts the driver to reconsider parking preferences during peak time ..........61
Figure 17 Graphical representation of a queuing model ...........................................................................62
Figure 18 Parking queuing model .............................................................................................................63
Figure 19 No guidance parking model ......................................................................................................64
Figure 20 OAPS parking model ................................................................................................................64
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Figure 21 SPDA parking model ................................................................................................................64
Figure 22 Vehicle arrivals (left), arrival rate (middle) and vehicle service times (right) for t=te ............66
Figure 23 Average wait time comparison for High Flux at t = te with a constant ƛ and t .......................67
Figure 24 Parking utilisation comparison High Flux at t = te with a constant ƛ and t .............................68
Figure 25 ƛ and t based on exponential distribution .................................................................................69
Figure 26 Average wait time comparison for High Flux at t = te where ƛ and t have exponential
distributions ......................................................................................................................................69
Figure 27 Parking utilisation comparison for High Flux at t = te where ƛ and t have exponential
distributions ......................................................................................................................................70
Figure 28 Average wait times for t > te .....................................................................................................71
Figure 29 Parking utilisation for t > te .......................................................................................................72
Figure 30 Average wait time for t >> te ....................................................................................................73
Figure 31 Parking utilisation for t >> te ....................................................................................................74
Figure 32 Average wait time for t < te .......................................................................................................75
Figure 33 Parking utilisation for t < te .......................................................................................................76
Figure 34 Average wait time for t << te ....................................................................................................77
Figure 35 Parking utilisation for t << te ....................................................................................................78
Figure 36 Visual representation of parking metrics ..................................................................................84
Figure 37 Simplified workflow of SPDA. For a detailed functional diagram, refer to Appendix A.4 ...88
Figure 38 Simulation Framework ..............................................................................................................89
Figure 39 Simulation Area ........................................................................................................................90
Figure 40 Parking Data Visualizer ............................................................................................................97
Figure 41 Comparative Report of five consecutive Saturdays for a segment of William Street. Time
sampled every 15 minutes. ...............................................................................................................98
Figure 42 Weekly Seasonal plot for parking occupancy ..........................................................................99
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Figure 43 Daily Seasonal Plot of parking occupancy ............................................................................ 100
Figure 44 Periodogram for parking occupancy signal ........................................................................... 100
Figure 45 Periodogram for smoothed parking occupancy signal .......................................................... 101
Figure 46 ACF plot of parking occupancy data ..................................................................................... 102
Figure 47 PACF plot of parking occupancy data ................................................................................... 103
Figure 48 Experiment layout in Azure Machine Learning Studio ........................................................ 106
Figure 49 (a) Boosted Tree Regression Model (b) Neural Network ..................................................... 107
Figure 50 (a) PFI workflow (b) TMH workflow ................................................................................... 108
Figure 51 Output of TMH module ......................................................................................................... 109
Figure 52 (Left) Output of PFI module (Right) Model evaluation and error histogram .................... 110
Figure 53 Comparison of actual (x-axis) vs. scored labels/predicted values(y-axis). The points along
black dotted line represent predictions with least amount of error. ............................................. 110
Figure 54 Output of Tune Hyper Parameters module showing optimal settings for Neural Net
Regression for highest value of Coefficient of Determination (R2) ............................................ 111
Figure 55 (Left) Output of PFI (Right) Model evaluation and error histogram .................................... 112
Figure 56 Comparison of actual (x-axis) vs. scored labels/predicted values(y-axis). The points along
black dotted line represent predictions with least amount of error. ............................................. 113
Figure 57 Integration layout ................................................................................................................... 115
Figure 58 Database layout ...................................................................................................................... 116
Figure 59 Parking Simulator graphical user interface ........................................................................... 116
Figure 60 Parking availability LF/HF .................................................................................................... 117
Figure 61 Parking availability MA/LF ................................................................................................... 118
Figure 62 Parking Availability MA/HF ................................................................................................. 119
Figure 63 Demand for low parking availability ..................................................................................... 120
Figure 64 Demand for medium parking availability .............................................................................. 120
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Figure 65 Search time for parking (LA, HF, HD, HV) .......................................................................... 122
Figure 66 Average distance to desired location (LA, HF, HD, HV) ..................................................... 122
Figure 67 Percentage of abandoned trips (LA, HF, HD, HV) ............................................................... 123
Figure 68 Search time for parking (HA, HF, LD, LV) .......................................................................... 124
Figure 69 Average distance to desired destination (HA, HF, LD, LV) ................................................. 124
Figure 70 Search time for parking (MA, LF, HD, MV) ........................................................................ 125
Figure 71 Average distance to desired destination (MA, LF, HD, MV) ............................................... 126
Figure 72 Search time for parking (Prediction vs. No prediction) ........................................................ 127
Figure 73 Average search time (unreliability in system inputs) ............................................................ 128
Figure 74 Average distance from destination (unreliability in system inputs) ..................................... 128
Figure 75 Search time comparison with OAPS (LA, HF, HD, HV) ..................................................... 129
Figure 76 Search time comparison with OAPS (MA, HF, LD, LV) ..................................................... 129
Figure 77 Parking Utilisation (Control, High Demand) ........................................................................ 130
Figure 78 Parking Utilisation (SPDA, High Demand) .......................................................................... 131
Figure 79 Parking utilisation (Control, Low fluctuation) ...................................................................... 131
Figure 80 Parking utilisation (SPDA, Low fluctuation) ........................................................................ 132
Figure 81 Detailed view of SPDA and interfacing with simulator ........................................................ 153
LIST OF TABLES Table 1 Summary of parking policy characteristics (Barter, 2009) .........................................................32
Table 2 Evaluation of studies based on VMS (Thompson & Bonsall, 1997) ..........................................44
Table 3 Active Guidance PGS and their features .....................................................................................47
Table 4 Results of 28 GPS-tracked journeys by Streetline Inc. ...............................................................49
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Table 5 Annual potential impact by extrapolating findings of Table 4 to 100,000 motorists using
Parker three times a week ................................................................................................................50
Table 6 Parking metric definitions ............................................................................................................83
Table 7 Parking Events Dataset Attributes ...............................................................................................96
Table 8 Parking Dataset Specifications .....................................................................................................96
Table 9 Top spectral densities of frequencies present in the parking occupancy signal ....................... 101
Table 10 Top spectral densities of frequencies present in the parking smoothed occupancy signal .... 102
Table 11 Dataset details for a given street segment ............................................................................... 106
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ABSTRACT
Balancing on-street parking supply with demand is a challenging issue faced by today’s
metropolis and fits perfectly the context of “tragedy of the commons”. Modern
transport planners are now turning to innovative technologies and re-thinking parking.
One of the emerging trends in parking management is the use of parking guidance
systems. Parking guidance equips drivers with parking availability information so they
can make smarter decisions about their parking choice. Recent studies have shown
positive effects of using such systems, such as reduced parking search time and
increased revenue generation. Due to lack of centralisation, parking guidance systems
broadcast parking information to all drivers (opportunistically assisted parking
guidance or OAPS). During times of high competition, this results in the
synchronization effect (a race condition where multiple vehicles compete for limited
spaces). This can create traffic bottlenecks and result in a frustrating parking guidance
experience, therefore negating the usefulness of such systems. The aim of this thesis is
to reduce synchronisation effect and time spent in cruising for parking by introducing
a selective parking discovery algorithm (SPDA) as the basis for parking assignment.
SPDA takes drivers preferences and outputs the ideal parking location instead of letting
drivers choose from a list of all available parking locations. This is made possible by
introducing parking occupancy forecasting using machine learning techniques. Parking
occupancy predictions show high accuracy with coefficient of determination (R2) up to
0.95 and mean absolute error (MAE) as low as 1.1. SPDA is tested against OAPS in
both theoretical and practical scenarios using real-world data. The results indicate
significant reduction in cruising time and synchronisation effect as compared to OAPS.
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CHAPTER 1. INTRODUCTION
1.1 Background
With the increase in car ownership levels, the number of trips made by private car
owners to the central business districts around the world has significantly increased.
But the amount of parking capacity has not kept up with the ever increasing demand
(Dell’Orco & Teodorović, 2005, Yan-ling et al., 2016, Wijayaratna, 2015, Ji et al.,
2014, Giuffrè et al., 2012, Edquist et al., 2012). According to Shoup (2006), a
considerable amount of traffic generated in these business districts is not caused by
drivers who are on their way somewhere but drivers that have already arrived and are
cruising in search of an available parking space. This phenomenon is a result of parking
spill over or failure to find a vacant parking spot, which leaves drivers with no option
but to cruise around their destination. Cruising for parking creates several problems for
both the commuters and the government. These problems include elevated congestion,
time and fuel wastage, carbon emissions, driver stress, illegal parking and reduced
social surplus. Hence, the decline in the quality of urban commute is a consequence of
ineffective parking management policy. A variety of parking policies can be
implemented to tackle specific requirements and ensure maximum parking occupancy
and spill over management (Barter, 2009).
Urban mobility relies on on-street parking as a cost effective and convenient solution.
Most drivers wish to park for a short duration but find themselves in a “trial and error”1
situation in search of available parking. Increasing parking capacity is not always
1 Trial and error method of problem solving is characterised by repeated, varied attempts which are continued until success, or until the agent stops trying.
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possible since it is not only costly but impossible due to lack of land and adverse effects
on the environment. Additionally, an increased supply of parking does not
automatically guarantee an effective supply. Parking policy alone cannot solve
rationing in an absence of supply mechanism. It is also challenging to measure, estimate
or predict effect of parking policy. Policy or infrastructure changes are introduced
gradually since drivers are likely to resist changes in parking ecosystem (Saltzman,
1994).
Emphasis on discouraging the “wrong” parking behaviour can be made possible by
educating driver about parking conditions so they are in a position to make better
decisions about their parking choice. With the introduction of smartphone and in-car
technology, parking management systems are harnessing the power of real time parking
information to help drivers make better parking choices. One of the emerging
technologies in parking management is the application of Geographic Information
Systems (International Parking Institute, 2013). GIS enabled parking spots are
discoverable by nearby drivers using specialized mobile phone applications or in-car
smart dashboards. These systems are collectively known as Parking Guidance Systems
(PGS). The primary objective of these systems is to reduce parking search time, which
consequently reduces traffic congestion, improves road safety and the environment.
1.2 Research Gap
Existing research on the issues regarding on-street parking is not only deficient but
conflicting. Marshall et al. (2008) noted the lack of definitive solutions to the on-street
parking crisis and attributed it to the scarcity of comprehensive research in this area.
To understand problems associated with on-street parking, research regarding parking
demand, parking rationing, pedestrian safety and efficiency of land use is paramount.
Finding solutions to these problems are challenging due to the multi-faceted nature of
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the issues arising from on-street parking. Often, researchers fail to account for a wide
spectrum of outcomes that result in ineffective real-world parking solutions.
On-street parking, once a free commodity became a public nuisance for modern cities
and was eventually subjected to restrictions as early as 1920 (Shoup, 2005). The notion
itself whether problems created by on-street parking outweigh benefits has been
contested and debated by researchers. Despite varying opinions, on-street parking
remains the most prevalent method to provide shared parking (Litman, 2006). Studies
by Shoup (2006) confirm higher demand of on-street parking as compared to the more
expensive off-street alternative. Jakle & Sculle (2004) recorded decline in retail
business as a result of ban on on-street parking since shoppers perceived absence of on-
street parking as an inconvenience.
Significant research work related to on-street parking was carried from the 1940s
though to 1970s however nothing substantial has been published since 1980s (Marshall
et al., 2008). It can be argued that only parking policy has been considered a popular
method of dealing with problems associated with on-street parking. For instance,
pricing is widely accepted as a tool to manage parking supply and limiting car use.
Marsden (2006) noted that the effectiveness of on-street parking pricing policy is a
topic that has received little research attention because parking problems are often
conflated with the overall trip. Additionally, overall road pricing policy is more
appealing as it is assumed to resolve wider aspects of urban transport problems,
including parking.
Whether or not pricing is effective when it comes to rationing parking is a politically
charged and geographically sensitive subject (IHT, 2005). Studies by Hensher & King
(2001) in Sydney, Bain (2002) in the UK and Shiftan (2002) in Israel present
contrasting outcomes when it comes to driver’s response to road pricing. Sydney
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drivers presented high sensitivity to elevated parking prices, far greater than in-vehicle
costs and travel mode choice. UK drivers demonstrated willingness to trade-off
convenience for low price however their elasticity towards mode of travel remained
low. In case of Israel, drivers responded to high prices by either changing their
destination or not making a trip at all. Pricing increase may reduce occupancy and hence
ease in finding on-street parking but more studies are required to measure the overall
socio-economic impact (Cats et al., 2016).
Road planning often ignores land use activities, such as on-street parking (Wijayaratna,
2015). Saltzman (1994) criticized flawed simulation techniques when it comes to
studying parking related transportation problems. He identified a large number of
papers considering the effects of various controls of traffic flow while others focus
mostly on off-street parking. Relatively, a smaller number directly addressed on-street
parking issues.
A comprehensive literature review on the issue of on-street parking crisis exposed a
gaping hole when it comes to innovation in the parking management. Shoup (2005)
emphasized on overcoming technological barriers in the parking industry. Huge returns
have been observed as a result of technological investment in parking sector, such as
parking meters and payment systems. Therefore, research is required to explore further
areas where smart technology can be harnessed to educate drivers and discourage the
“wrong” kind of behaviour. On-street parking in many highly urbanized dwellings is a
trying experience and requires creative and even controversial solutions. Policy alone
appears to be clearly inadequate (Saltzman, 1994). Research is required to study the
effectiveness of parking information systems, such as websites and smartphones, that
help drivers locate vacant parking spots (Brooke et al., 2014).
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1.3 Aim of research
The aim of this research is to develop a parking guidance system that mitigates reduces
the time spent on cruising for parkingnegative effects of on-street parking on urban
transport. The proposed system focuses on the information flow process between the
driver and the on-street parking inventory in parking guidance systems. The primary
objective will be to reduce parking search time using a parking assignment algorithm,
which not only ensures cooperation between competing drivers for limited on-street
parking spaces but also encourages a sustainable transport model by giving drivers pre-
trip parking insight. Parking guidance information will be exposed via a device agnostic
API (application programming interface) which can be implemented into web
applications, mobile applications or in-car navigation systems.
The “selective” approach towards broadcasting parking availability information is a
shift from traditional parking guidance which relies on driver decisions when it comes
to choosing a parking location based on the type or level of guidance provided. In
contrast, in a selective scenario, the decision-maker role will be reversed. Instead of the
driver choosing where to park, the parking location will choose the most suitable driver
selectively by advertising itself only to that driver (Figure 1).
An on-street parking location or a cluster of parking locations can be thought of as a
self-contained ecosystem. Based on local conditions of the ecosystem, the selective
algorithm will consider driver attributes such as the travel distance, the length of stay,
walking distance and cost of parking.
The end product will be a series of independently functioning parking ecosystems that
are not just collaborating with drivers but also among each other in order to
accommodate and distribute incoming drivers over free parking spaces. The hypothesis
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that advanced knowledge of parking conditions reduces cruising time, improves driver
experience and increases social surplus will be tested against an established criteria and
simulation.
Figure 1 An overview of Selective Parking Discovery
A: In absence of centrally controlled guidance, vehicles discover parking spots opportunistically. During times of high competition, multiple vehicles can potentially drive towards limited parking
spaces resulting in a race condition (synchronization effect). B: Each vehicle is centrally allocated parking using selective discovery, therefore removing
synchronization effects and reducing total distance travelled in search of parking.
1.4 Significance of research
Recent advances in technology have opened many doors for implementation of
innovative solutions to problems of urban transport including parking. Being able to
commute and park is an integral part of a city dweller’s life. An average car is parked
96.5% of the time and only actually used the remaining 3.5% (Bates & Leibling, 2012).
With increasing population and car ownership levels, making parking safe and
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convenient is becoming an ever-growing concern for city planners. To tackle the
problem of on-street parking, various innovative systems have been introduced in major
cities all over the globe (Figure 2).
Figure 2 Cities around the world that are using Parking Guidance Information Systems: San Francisco, New York, Seattle, Los Angeles, Washington D.C., Portland, Ore., Miami, Houston, Boston,
Denver, Pittsburgh, Tampa, London, Barcelona, Paris, Tokyo, Canberra
According to findings of the International Parking Institute (2013), topping the list of
trends in the $30 billion parking industry is the “move toward innovative technologies
to improve parking access control and payment automation”. Another top trend is “real-
time communication of pricing and availability to smartphones” (Figure 3).
Today, parking is about so much more than storing cars. It’s
central to the creation of liveable, walkable communities. It’s about
cars, bikes, mass transit, mobility, and connecting people to places.
- (International Parking Institute, 2013)
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Figure 3 Emerging trends in parking (International Parking Institute, 2013)
This shift in trend is due to the realization that drivers are often unaware about desired
parking locations, their hours of operation, parking tariffs and most importantly, if the
parking spaces will be available when they get there. Drivers can potentially change
their mode of travel if they have pre-trip information. Effective parking guidance and
education can help drivers steer away from congestion and demand spilling over
supply.
In most cities today, on-street parking is rationed in an unreliable and inequitable way.
A better parking guidance system would raise the probability of finding a vacant
parking space by effectively rationing parking supply. This would not only reduce
driver angst for parking search but also moderate traffic congestion and improve
pedestrian safety (Saltzman, 1994). Parking information systems already in place have
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shown undeniably positive outcomes (Idris et al., 2009). A report published by
Navigant researched smart parking technology and estimated that sensor-enabled on-
street parking spaces will surpass one million worldwide by 2024 (Navigant, 2015) as
shown in (Figure 4). The next wave of parking guidance technologies are already set
to be integrated into in-car technology (Kodransky & Hermann, 2011).
Figure 4 Annual Smart Parking Systems Revenue by Region
World Markets: 2015-2024 (Navigant, 2015)
My thesis is an effort to not only fill gaping holes in the area of innovative on-street
parking research but practically employ latest advances in transport technology to solve
the parking problem of today and the future. Self-driving “smart car” projects by
leading companies like Tesla, BMW and Google represent the future of automotive
industry (Newstex, 2016). Likewise, General Motors and Mercedes-Benz have been
experimenting with “driver-assisted” technologies since 2000’s (Poczter & Jankovic,
2014). Self-driving cars will require a smart parking guidance component so these cars
can not only drive themselves but also park on their own. Selective discovery is a
24
concept that can solve the autonomous parking problem by ensuring cooperation
between self-driving vehicles.
The scientific contributions of my thesis are summarized below:
1. The concept of selective discovery where parking availability information is
withheld and released to only qualifying vehicles (based on their parking
requirements) to achieve system-wide optimization.
2. Development of Parking Data Visualizer (PDV) tool to analyse trends in
parking occupancy.
3. Methodology for parking occupancy forecasting as a pre-trip information tool
for drivers using machine learning from parking sensor data.
4. Low level design of parking guidance information system (architecture and
algorithm).
5. High level parking guidance information system design (API and network
communications).
1.5 Thesis structure
CHAPTER 1: INTRODUCTION
CHAPTER 2: LITERATURE REVIEW
To understand the scale, cause and effects, relevant literature is reviewed to assess the
current parking problems faced by today’s metropolis. Topics of relevance include:
A comprehensive review of causes of parking problem and associated impacts
on transport infrastructure.
Parking policies as a tool to reduce congestion caused by wasteful parking
search.
Driver psychology with regards to parking search preferences.
25
Driver parking search behaviour in response to parking policies and pre-trip
information.
A comparative review parking guidance systems and associated technology.
The review assists in understanding the fundamental causes of parking problems, its
effects and identifies areas requiring improvement using parking guidance.
CHAPTER 3: SELECTIVE PARKING DISCOVERY ALGORITHM DESIGN
The primary logic of the guidance system is developed and presented as flowcharts and
pseudo code based on the problems and limitations identified in literature review. A
methodology to forecast parking occupancy as a pre-trip guidance tool is presented.
The presented methodology is then tested against parking occupancy data from
Melbourne using machine learning techniques.
CHAPTER 4: FORECASTING CASE STUDY &. SIMULATION
Program logic is implemented using PHP and MySQL. An application-programming
interface (API) is developed to take driver input and return output (available parking
options/alternatives) as JSON (JavaScript Object Notation). Additional technologies
include:
Mapping API for plotting geographic data.
Routing and distance API for travel times.
The performance of the proposed system is tested, validated and optimized using a
custom-built simulation environment. Based on the simulation results against a variety
of attributes, the guidance system undergoes iterative refinements to achieve optimal
network performance. Simulation results are analysed to evaluate feasibility and
usefulness.
26
CHAPTER 5: SUMMARY AND CONCLUSIONS
I conclude the thesis by reviewing:
Purpose of this study
The parking problem and its contributing factors
Current parking guidance systems, their efficiency and limitations
Selective parking discovery algorithm and simulation findings
Parking occupancy forecasting methodology and case study findings
Implications of research
Limitations of research
Recommendations for further research
Final remarks
27
CHAPTER 2. LITERATURE REVIEW
The aim of this chapter is to review the multifaceted problem of parking and measures
taken by the transportation industry to address these problems associated with parking
(Figure 5). The key areas of this review include:
1. Parking problem and its effects
2. Parking policy
3. Driver behaviour
4. Parking choice modelling
5. Parking guidance systems
I begin by reviewing the parking problem and its effects on traffic. Since policy plays
a pivotal role in the effectiveness of transportation systems, I have investigated various
parking policies and examined their effectiveness. Furthermore, I have determined the
impact of parking policy on driver’s behaviour based on factors such as parking price,
location and walking distance. Insights from parking choice behaviour are utilised to
study their application in parking choice modelling and simulation. Finally, I have
reviewed current parking guidance systems, their effectiveness and limitations.
Figure 5 Venn diagram of the multi-faceted problem of parking
28
2.1 The parking problem and its effects
High growth in the volume of traffic due to urbanization and increased car ownership
is a global phenomenon. Population density is out of synchronisation with creation of
new streets and road infrastructure to accommodate private car owners in an equitable
way. As a result, matching parking demand with supply has become a major concern
for today’s transport policy makers (Manville & Shoup, 2005).
Drivers with knowledge and experience park often and vary their parking location
according to destination (Khattak & Polak, 1993). Most drivers, however, are often
unaware of desired parking locations, hours of operation, parking tariffs, and, most
importantly, if the parking resources will be available upon arrival (U.S. Department of
Transportation, 2007). Due to information asymmetry and absence of pre-trip parking
information, drivers have to drive directly to the parking resource to assess the situation.
Regular commuters (such as those travelling for work) occupy a large chunk of parking
inventory, which leaves inadequate spots for occasional visitors commuting for leisure.
Due to expensive off-street parking, drivers continue to cruise for on-street parking. In
most cases, parking shortage encourages illegal and double parking (Arnott & Inci,
2006). During peak hours, most on-street parking spaces are occupied. On the other
hand, off-street car parks and parking garages are never 100% occupied (Simićević et
al., 2013).
Based on the aforementioned literature, parking problems can be categorised into:
Supply demand: not enough supply of parking.
Discovery: complex parking design making it challenging for drivers to find
free spots.
Pricing: parking too expensive.
29
2.1.1 Impact of Cruising
Vehicles cruising for parking make up to 30% of the city traffic and actively contribute
towards congestion (Shoup, 2006). Besides congestion, Gantelet & Lefauconnier
(2006) and Höglund (2004) have emphasized other effects of cruising that include lost
time, fuel wastage, noise pollution and greenhouse effect. Shoup (2005) extrapolated
CO2 emissions for a total of 3600 vehicle daily miles travelled cruising for parking in
Westwood, United States. Over the course of one year, this totals to 945,000 extra miles
travelled consuming of 47,000 gallons of fuel and producing 728 tons of CO2.
Additionally, an active parking search makes drivers reduce their speed in order to give
themselves an increased reaction time which also increases overall travel time (Guo et
al., 2011) and amplifies CO2 emissions (Barth & Boriboonsomsin, 2009) as shown in
Figure 6.
Figure 6 Potential CO2 emissions savings through congestion mitigation on Interstate-110 in downtown Los Angeles (Barth & Boriboonsomsin, 2009) .
30
2.1.2 Impact on Road Capacity
The parking problem has caught city planners by surprise and this unpreparedness has
resulted in sharp contradictions between parking demand and supply (Yan-ling et al.,
2016). Congested commute is not only a source of stress and anxiety in itself but also
has the tendency to cause road accidents and can potentially manifest itself in social
life and absenteeism from work (Cassidy, 1992).
Besides parking spill-over, on-street parking also contributes to reduced road capacity
by as much as 45% (Jakle & Sculle, 2004). On-street parking can be unsafe, prone to
crashes, and subject to increased congestion (Marshall et al., 2008). A study by the
Australian Bureau of Transport and Regional Economics estimated the total avoidable
cost of congestion in 2005 for Australian cities was $9.4 billion. Construction of new
road infrastructure or expansion of existing roads is usually costly, time consuming and
infeasible so the alternative is to improve the capacity of the existing road infrastructure
by application of congestion management strategies and policies (Wijayaratna, 2015).
On the contrary, Taylor (2002) proposed that congestion is an inevitable by-product of
a thriving metropolis and compared it to long queues in front of a restaurant or theatre
box offices as a sign of success.
2.1.3 Impact on Road Safety
The myriad of on-street parking challenges has even tempted researchers to reconsider
on-street parking i.e. where curb-parking should be provided or prohibited (Box, 2004).
Safety studies between 1965 and 1966 across American cities attributed 16% of the
crashes with on-street parking (HRB, 1971). Highway Research Board’s report
concluded that the best and most economical method of improving economy and
improving road safety is removal of on-street parking. Vehicles parked on a complex
31
on-street urban environment increases drivers workload, depleting their ability of
sensing peripheral hazards (Edquist et al., 2012). However, both Taylor (2002) and
Marshall et al. (2008) argued against the faster speeds and quick dispatch of traffic
through busy cities. Marshall et al. (2008) presented the counter argument that the
absence of on-street parking results in higher speed limits. Consequently, the fatal crash
rate was observed to be twice as high as compared to on-street parking.
2.2 Role of parking policies in parking problem
To reduce the problems associated with parking, various parking policies have been
implemented around the world. Parking rules, such as pricing and schedule, have a
direct impact on parking utilisation. In the following sections, I discuss the effects of
parking policies on parking utilisation, challenges in parking policy design and
unpredictable effects of commonplace parking policies.
2.2.1 Effectiveness of parking policies
While reviewing causes of parking crisis, it is important to assess parking policies as
they have strong and complex impact on the operation of the parking subsystem. An
ineffective parking policy is a consequence of problematic trade-offs between
regeneration, restriction and revenue of parking (Marsden, 2006). Overall, parking
policies differ based on the socio-economic model of a particular geographic area.
Based on these characteristics, Barter (2009) compiled and contrasted parking policies
from United States and Australia (Table 1).
In Australia, parking in the suburbs uses the conventional approach whereas central
business districts attract the parking management style of parking policy. In the UK,
parking management is used to provide short term parking in the city and long term
32
parking on the outskirts (Bates & Leibling, 2012). It legislates parking using the
following practices:
Pricing
Remote payment
Enforcement
Environmental initiatives
Review of parking restrictions
Holiday, Sunday and night-time enforcement.
Conventional Parking Management
Market Oriented
Characteristics Development provides onsite parking for demand
Fixed number of spaces per unit, floor area etc.
Dynamic pricing & stay times
Caters to peak traffic demand
Zoning
Allows market to achieveefficient supply & demand
Based on performance pricing
Optimizes parking itself,without seeking other objectives
Features Easy to implement Shared parking
Dynamic
No spill over, hence no cruising for parking
Limitations Non-dynamic
No Shared Parking
Prone to controversy Risky
Politically challenged
Table 1 Summary of parking policy characteristics (Barter, 2009)
2.2.2 Challenges in parking policy design
The complexity in parking policy design exists due to the heterogenic nature of drivers
(Simićević et al., 2013). Feeney (1989) recognized imprecise price elasticity estimates
in formulating parking policies and stressed that more attention should be given to
before and after studies when it comes to:
1. definition of demand variable (i.e. type of vehicle use)
33
2. possible substitution effects of parking demand2
3. monetary (e.g. parking prices, toll) and non-monetary implications (e.g. time
and importance of trip) associated with available travel options
4. impact on parking supply when there are reasonable competing alternatives
Parking pricing as a constituent of parking management to control parking demand is a
popular choice among policy designers (Barter, 2009) however it ignores driver subsets
and different market needs (Kelly & Clinch, 2006). Marsden (2006) noted that parking
restrictions, at present, attempt to trade-off revenue generation with keeping private
cars away from the city. Theoretical constructs demonstrate arguments for using
parking policies to address congestion and parking rationing, actual circumstances
where commuters pay the “true cost of parking” do not exist (IHT, 2005, Shoup, 2005).
2.2.3 Parking Policy Outcomes: Expectation vs. Reality
Relying on parking pricing only poses the potential to negatively affect business
efficiency (Luca et al., 2006). Undesired long term effects of increased parking prices
can cause equity problems such as mobility segregation and encourage city centres to
become only inhabited by high-income drivers (Gantelet & Lefauconnier, 2006). There
may be a threshold point at which pronounced varied effects between different driver
classes based on income can occur. As policy designers are tempted to increase parking
prices as a “quick fix” to balance supply and demand, this could be an area for future
concern (Kelly & Clinch, 2006). On the far side of the scale, free or over provision of
2 The substitution effect is the idea that as prices rise (or incomes decrease) consumers will replace more expensive items with less costly alternatives.
34
parking is also a concern as it encourages further car dependence and lowers urban
development density (Glazer & Niskanen, 1992, Shoup, 2005).
A Transit Cooperative Research Program (TCRP) review attributed increasing parking
prices with shifts in commuter’s choice for parking location rather than choosing a
different mode of travel (Marsden, 2006). Albert & Mahalel (2006) reached a similar
conclusion i.e. high levels parking demand elasticity is demonstrated by commuter
readiness to pay higher parking fees (in contrast to congestion tolls). Furthermore,
stricter enforcement has achieved less than ideal results. Royal Automobile Club
(RAC) Foundation reported 48% respondents of a parking survey acknowledged
having parked illegally (Bates & Leibling, 2012).
2.3 Driver Behaviour
Findings of Section 2.2 provided an insight into the shortcomings of parking policies
due to driver heterogeneity. To analyse this further, I have reviewed parking behaviour
and search patterns of various driver classes in response to parking features such as
price, walking distance and location.
2.3.1 The Parking Search Spiral
To make urban transportation better, it is important to understand parking choices of
travellers (Lam et al., 2006). On-street parking search behaviour is comprised of a
number of factors that vary in magnitude depending on the driver’s background and
preferences. These include driving time, parking price and walking time (Chen, 2007).
Different levels of income also affect parking choices. High-income drivers choose the
parking lots with shorter walking distance. Drivers whose parking fee is paid by their
employers are indifferent to it, but are affected by walking time (Xie & Lei Sun, 2011).
35
While searching for available parking, a typical driver circles around the desired
destination in a spiral fashion (Figure 7). Based on the nature of travel, knowledge of
parking conditions and psychological state, the driver “switches” her preferences over
elapsed search time in the following order (Gantelet & Lefauconnier, 2006):
1. Free parking
2. Paid parking
3. Unauthorized parking
4. Off street parking
5. Abandon trip/choose public transport
Figure 7 Driver preferences and the parking search spiral (Gantelet & Lefauconnier, 2006)
Other notable factors include (Scholefield et al., 1997):
parking type
parking location
parking duration
occupancy levels
destination
travel frequency
travel time
36
available routes.
Drivers, who commute daily for work, are more sensitive to these factors (Simićević et
al., 2013). Drivers have also been found to prefer pre-trip information in favour of
driving directly to the location (Polak & Jones, 1993). Drivers who plan their journeys
around peak hours experience longer parking search times and may reconsider their trip
preferences in future to avoid cruising for parking (Lam et al., 2006).
The probability of trip abandonment is found to be much lower than choosing public
transport as an alternative (Simićević et al., 2013). An often-ignored aspect of driver
behaviour is rationality and optimism. Highly optimistic drivers choose to search for
parking in close proximity to their destination. Less optimistic drivers prefer to park at
a location of lower demand, which is usually further away from their destination (Guo
et al., 2012). Previous number of parking rejections has also been found to play a role
in this regard (Dell’Orco & Teodorović, 2005).
Punctuality at workplace has also been identified as a factor in parking search behaviour
(Brooke et al., 2014). Daily commuters are less likely to cruise for parking as compared
to those travelling for leisure (Van Ommeren et al., 2012). Furthermore, a commute is
abandoned when the monetary and non-monetary cost of parking search (trip utility)
exceeds expected gain (Richardson, 1982).
A study by Bonsall & Palmer (2004) demonstrated that parking habits play an important
role in parking search behaviour, especially among low income drivers whereas higher
income drivers alter their preference in order to minimise walking distance to their final
destination. Gender and age have also been shown to have an effect on parking choice
behaviour.
37
Simićević et al. (2012) conducted a study to manage parking demand by controlling
parking pricing. Information on users' response to parking price changes was collected
using face-to-face interviews. Approximately 56% of drivers stated that they would not
change their model of travel (i.e. by car) to the CBD regardless of parking cost.
Nonetheless, the controlling parking price provided a balance between parking demand
and supply between 84% and 98% of utilisation of the available number of parking
spaces. Antolín et al. (2018) took at in-depth look at factors that affect parking search.
The users of paid on street parking and paid underground parking were shown to have
a much lower perception of cruising time than those of free on-site parking, who are
much more willing to spend more time cruising for parking. If arrived early, users of
paid on-site parking demonstrated willingness to change to the alternative park and ride.
In the case of late arrival, drivers were shown to pay more for parking and opt for paid
underground parking. Chaniotakis & Pel (2015) studied drivers’ willingness to accept
cruising for parking. Uncertainty in parking availability was found to be important for
parking location choice. Parking availability after 8 min of cruising ranks second most
important factor in determining drivers’ parking decisions, whilst parking availability
upon arrival ranked fourth.
2.3.2 Choice between On-street and Off-street Parking
A parking survey conducted by Golias et al. (2002) concluded that a commuter’s choice
between on and off-street parking is strongly impacted by its cost. However, another
conclusion drawn from this survey is that parking choice is not impacted by driver or
trip characteristics. This point is highly contested by other researchers. Individual
circumstances, such as purpose of travel (work or leisure), income and whether the
parking costs are covered by commuter’s employers, are important factors in parking
38
preference (as discussed in section 2.3.1). Workers may prefer on-street employer-
arranged parking for its attractiveness, such as easier accessibility smaller delays going
in and out of the parking space (Hunt & Teply, 1993).
Calthrop & Proost (2006) concluded that if the price difference between on and off-
street parking was eliminated, drivers will break from wasteful searching and drive
directly to off-street parking lots. This conclusion, however, is contested by Kobus et
al. (2013) arguing that drivers are willing to pay a street parking premium since it
minimizes walking distance to their final destination. On and off-street parking rather
compliment and not substitute each other. Therefore, despite a closing gap between on
and off street pricing, cruising for parking may still occur, especially if the number of
on-street parking spaces is not increased (Arnott & Rowse, 2009). An explanation for
this contrast is social and geographic differences among cities which require different
approaches to parking policies, therefore, yielding varying and unexpected results
(Marsden, 2006, Barter, 2012).
2.3.3 To walk or not to walk?
Similar to preference between on and off-street parking, walking preferences are also
influenced by a driver’s individual characteristics and socio-economic environment.
Marsden (2006) reviewed studies of walking habits of drivers across 111 cities in US
and found that individuals walked between 3 to 7 minutes for work purposes. However,
these numbers could vary depending on the size of the urban area as urban density
correlates directly with walking distances. In Israel, 47% commuters walked for 5
minutes, 39% walked between 5 to 10 minutes and 14% walked over 11 minutes
(Shiftan, 2002).
39
Walking from the parking location to final destination, though an inconvenience, is
found to be a better alternative to public transport among drivers. Furthermore, drivers
park even further out if the parking is free (Rye et al., 2006). Parking preference studies
by Axhausen & Polak (1991) concluded that drivers valued walking time more over
parking search time.
2.4 Parking Choice Modelling
Traffic simulation requires mathematical modelling of transportation systems, such as
roundabouts, arterial roads and freeway junctions. This type of modelling is also
utilised to study effects of parking policies and parking guidance infrastructure based
on parking choice behaviour described in section 2.3. The following sections review
parking models and challenges associated with modelling parking behaviour.
2.4.1 Parking Choice Models
Mathematical modelling and simulation are popular tools used by transport planners to
study the effects of different types of parking policies. Using game theory, the utilities
of all players can be defined and the rules of the game set. A parking game can be
classified as a N+1 Stackelberg game where one player (transport authority) sets the
rules and other players (drivers) follow these rules (Hollander et al., 2006). N travellers
are divided into a number of groups (as discussed in section 3.2.2) depending on their
value of time (VOT). The interest of the transport authority may be to raise revenues or
to minimize congestion and/or total travel time over the network (Joksimovic, 2007).
Parking game is non-cooperative in nature i.e. N players compete against other for a
common resource to maximize their utility, also referred to as “Tragedy of the
commons” (Hardin, 1968). Finally, the model can be optimized to achieve Pareto
optimal equilibrium (Anderson & de Palma, 2004).
40
Washbrook et al. (2006) estimated a model based on 548 commuters from a Greater
Vancouver suburb as part of a discrete choice experiment in which they opted for travel
modes when choices varied in terms of travel time and cost, including parking charges.
Benenson et al. (2008) developed an agent-based parking search model PARKAGENT
which simulates the behaviour of each driver in a spatially explicit environment. The
model considers search attributes such as search time, walking distance, and parking
costs based on driver groups. The model indicates that additional parking supply
linearly affects the occurrence of extreme values but has only a small effect on the
search time for a parking location or walking distance between the parking location and
destination. Dieussaert et al. (2009) developed a project called SUSTAPARK to
simulate the traffic effects of parking search behaviour. This is based on an agent-based
model where drivers move based on purpose of the trip and daily activity schedule. It
uses cellular automation is used to map vehicle movements into a computer program.
Waraich & Axhausen (2013) developed parking model that integrates into an agent-
based traffic simulation so that the overall simulation can react to parking demand and
supply. The model demonstrates capability of incorporating parking capacity and
pricing. Sun et al. (2016) extended existing parking equilibrium studies by analysing
the impact of parking duration on demand and supply. The study modifies the parking
search function and proposes a user equilibrium model for general parking systems.
The model also demonstrates significance of value of time, parking cost and parking
duration as an influential factor when choosing a parking location.
Other models have been developed by Hensher & King (2001), Arnott & Inci (2006),
Ottomanelli et al. (2011), Gallo et al. (2011) and (Li et al., 2014) to demonstrate effects
of parking policies on driver behaviour, congestion, balancing supply and demand and
economy.
41
2.4.2 Challenges in Modelling Parking Search Behaviour
Waterson et al. (2001) has emphasized that driver choice behaviour is an outcome of
highly complex decision processes. It is challenging, if not impossible, to model
relationship between traffic authorities' actions and individual (drivers) behaviour
(Dell’Orco & Teodorović, 2005). To reduce model complexity, parking models often
suffer from assumptions and behavioural exclusions, which may affect their credibility
and performance in a real-world setting (Guo et al., 2012). This in-turn affects the
reliability of parking guidance models based on parking behaviour models. Often in
particular equilibrium analysis in non-cooperative game theory, such behavioural
assumptions are inappropriate (Kreps, 1990).
2.5 Parking Guidance Systems
In the previous sections, I reviewed the role of parking policy with regards to the
parking problem. In this section, I have investigated the role of parking guidance
systems as a tool for parking management and examine their effectiveness in reducing
cruising for parking.
Parking guidance systems (PGS) or parking guidance information systems (PGIS)
present drivers with information on parking occupancy. The technology involves use
of vehicle detectors, such as cameras or in-ground road sensors to detect the presence
of a parked vehicle and dissipate that information to the public domain via digital
signboards (variable message signs or VMS) and over the web via the internet. A
typical PGS architecture (Figure 8) serves 4 basic purposes (Teng et al., 2008):
1. Pre-trip travel information
2. Traveller services information
3. Travel demand management
4. Traffic control
42
The components of PGS can also be summarized into 4 major components (Idris et al.,
2009):
1. Information delivery mechanism
2. Information collection mechanism
3. Information control/processing
4. Inter-communication network
Figure 8 Typical PGS architecture (Teng et al., 2008)
The motivation behind PGS is to reduce parking search time, make efficient use of land,
increase revenue, improve the environment and increase road safety (CHAN et al.,
2001, Liu & Lu, 2005, Liu et al., 2006, Teodorović & Lučić, 2006, U.S. Department of
Transportation, 2007, Wenhong et al., 2008, Idris et al., 2009, Yikui & Yongyun, 2009,
Geng & Cassandras, 2012).
Based on how parking occupancy information is dispensed, they can be categorized as
active or passive (Jun, 2010). Passive systems broadcast parking information to a group
of users by utilizing variable message signs (VMS), radio broadcast and pamphlets
43
(Khattak & Polak, 1993) whereas active guidance systems offer specific parking
availability and guidance information to drivers based on their individual preferences
(using mobile or in-car devices) in a controlled manner. PGS can also be classified into
in-garage or on-street specific systems. Table 2 shows a summary of different parking
guidance techniques based on their features and services provided.
Technologies Features Services Provided
Agent Based Dynamic Distribution and Complex Traffic Environments
Bargaining, parking guidance and route negotiation etc.
Fuzzy Based Human-like intelligence and expertise
Intelligent parking methods e.g. parallel parking and perpendicular parking etc.
Wireless Sensor Based
Low cost implementation with lower power consumption
Detection and monitoring of the parking facility etc.
GPS Based Real time location based information and guidance towards destination
Provides information about the locality and availability of parking facility
Vehicular Communication
Provision of parking information distribution service for mobile vehicles
Antitheft protection, real time parking navigation service etc.
Vision Based Good for car searching in large parking lots
Lot occupancy detection, parking space recognition, parking charges collection etc.
Table 2 Parking Guidance system techniques (Faheem et al., 2013)
2.5.1 VMS based Passive Guidance
VMS is popular technology which has been used to provide parking information for
many years. VMS can be used either on the road or within car parks to direct drivers
towards vacant parking spaces (Figure 9). Vehicle detection systems are installed at the
entry/exit points of parking garages. In case of a non-isolated parking, various types of
vehicle detection technologies are used to detect presence of a vehicle at a parking spot.
These detectors include active infrared sensors, inductive loop detectors,
44
magnetometers, magnetoresistive sensors, weigh-in-motion sensors, RFID, video
imaging sensors etc. (Idris et al., 2009).
Figure 9 An Example of VMS (SIEMENS, 2011)
How effective is VMS based PGS?
Thompson & Bonsall (1997) concluded that VMS based PGS only play a supplemental
role in altering drivers’ choice of parking. VMS display generalized information, which
may or may not be useful for a particular driver. This is also due to lack of awareness
about the benefits of these systems. Hence, drivers often ignore the benefits of VMS
(Table 3).
Table 3 Evaluation of studies based on VMS (Thompson & Bonsall, 1997)
45
Waterson et al. (2001) derived similar results by stating that the magnitude of reduced
travel time was small, with reductions in total travel time for all drivers in the range
0.1– 1.0% corresponding to economic benefits of up to £500 per day for the test network
of approximately 40,000 vehicles. The study confirmed the effects of parking guidance
information observed in earlier studies, but the network-wide level impact was severely
limited. Shaheen et al. (2005) conducted a survey, which revealed that drivers perceived
VMS as advertising and unclear.
Implementation of VMS based PGS is popular in isolated off-street parking
environments such as garages and university campuses. On campus implementation
study by Fries et al. (2011) noted 15% reduction in network delay and a potential
$20,000 in savings for commuters. Optimization studies have shown demonstrable
effects on VMS efficiency based on where they are placed (Ni et al., 2015). Similarly,
optimisation of occupancy information shown based on the nature of urban activity has
also been analysed (Lv et al., 2012).
Limitations of VMS based PGS
Performance of passive guidance suffers due to its inherent limitations. Jun (2010)
proposed that once parking information is broadcasted in a non-personalized or
uncontrolled manner, drivers have to make their own parking choices. This may result
in the following scenarios:
1. By the time the driver arrives as the parking spot, it is already occupied (due to
arrival delay).
2. A VMS sign can potentially attract too many vehicles to a parking spot leading
to heavy localized traffic.
46
2.5.2 Active Guidance
Active guidance is based on a geographic information system (GIS) that disseminates
dynamic parking availability information based on driver preferences such destination
location, drive time, parking cost and walking distance (Figure 10). As opposed to
broadcasting information (in case of VMS), active guidance can control the information
provided. Parking occupancy information can be used by drivers via personal
computing devices or in-car devices (such as smart dashboards and GPS) over the
internet. A major advantage of such systems of passive guidance is that the driver does
not have to drive to the location of a VMS sign to investigate parking availability and
hence the decision can be made before making the trip to the destination.
Figure 10 PGS based on web and GIS (Liu et al., 2006)
Author Title Core Design Features
Geng & Cassandras (2012) A new “Smart Parking” System Infrastructure and Implementation
‐ 2-way communication vehicle to infrastructure communication
‐ Optimal allocation of parking & reservation guarantee
47
Dou et al. (2011) The Service System of Urban Parking Guidance Based on GIS in Lianyungang, China
‐ Parking availability prediction ‐ Optimal allocation of parking ‐ Optimal route calculation
Yikui & Yongyun (2009) Design of Parking Guidance and Information System in Shenzhen City
‐ Parking availability prediction
Yang et al. (2009) Intelligent Parking Negotiation Based on Agent Technology
‐ Price negotiation ‐ Route negotiation
Teodorović & Lučić (2006) Intelligent parking systems ‐ System “learns” from user experience to predict future parking inventory conditions
Liu et al. (2006) Design and Development of Parking Guidance Information System Based on Web and GIS Technology
‐ Parking availability prediction ‐ Membership options (logins for user
and admins)
Table 4 Active Guidance PGS and their features
Active PGS controls parking rationing by introducing a central information processing
server. The parking information dissemination algorithm that runs on this server can be
fine-tuned to serve various functions (CHAN et al., 2001, Hui-ling et al., 2003, Liu &
Lu, 2005, Liu et al., 2006, Teodorović & Lučić, 2006, Leephakpreeda, 2007, U.S.
Department of Transportation, 2007, Wenhong et al., 2008, Idris et al., 2009, Jun et al.,
2009, Yang et al., 2009, Yikui & Yongyun, 2009, Jun, 2010, Dou et al., 2011, Geng &
Cassandras, 2012), such as:
scramble drivers in search of parking to reduce congestion
maximize revenue by efficiently allocating parking supply (utilization rate)
make adjust parking prices dynamically to encourage or discourage drivers
predicting parking availability by analysing historic trends
reserving parking spaces in advance (in off-street closed systems like garages)
Kokolaki et al. (2012) categorized active guidance search into 2 types based on how it
dispenses parking availability information:
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1. Opportunistically assisted parking search (OAPS)
2. Centrally assisted parking search (CAPS)
OAPS is a result of global parking knowledge evenly broadcasted to all drivers
querying for parking occupancy. The limitation of this approach is the synchronization
effect i.e. in times of higher parking demand, OAPS can dispatch a higher number of
vehicles towards which is greater than parking lot capacity.
CAPS uses a more conservative approach towards dissemination of parking occupancy
information. Instead of global broadcast, it serves parking occupancy to drivers on an
individual basis. This way, the system is able to control the number of vehicles driving
towards a particular parking lot. This method has been found to mitigating effects on
synchronization in simulations performed by Kokolaki et al. (2012).
Figure 11 Two popular apps that use OAPS. Left: Parker Mobile app by Streetline, Inc (Streetline, 2013)
Right: ParkRight mobile app by the City of Westminster (Westminster, 2016)
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Effectiveness Studies
GIS based PGS, broadly known as active guidance systems are fairly new and therefore,
peer-reviewed literature on the performance of on-street parking guidance systems is
lacking. As previously discussed, game theoretic models have been utilized to
analytically deduce the benefits of such systems installed at off-street parking garages.
However, these models have also exposed a fundamental shortcoming which is
ensuring that drivers cooperate with each other for limited parking spaces given the
demand as pointed out by Kokolaki et al. (2013b).
Other than Japan, United States and some parts of Europe, implementation of advanced
GIS based PGS system is still scarce in the rest of the world. Reports published by U.S.
Department of Transportation (2007) and Rodier et al. (2008) have confirmed a
decrease in the amount of parking search time and increased driver satisfaction after
introduction of these systems. A similar parking guidance system was recently
introduced in London, UK (BBC, 2014), however, an after-implementation study has
not yet been released as of the writing of this thesis.
Internal studies conducted by Streetline Inc., makers of the Parker mobile application
published their findings about the positive outcomes of using their mobile application
to locate unoccupied parking spaces (Streetline, 2013), as shown in Table 5 and Table
6.
Driving time was reduced by 43% from 6:26 minutes to 3:41 minutes
Vehicle miles travelled dropped 21% from 0.91 to 0.72
Average price of space per hour was lowered by 22% from $2.68 to $2.10
Table 5 Results of 28 GPS-tracked journeys by Streetline Inc.
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712,956 fewer vehicle hours on the road
3,021,964 fewer vehicle miles driven on city streets (4,863,380 km)
177,763 fewer gallons of gasoline used (672,906 litres)
3,142,843 pounds of CO2 emissions reduced (1,425,570 kg)
Table 6 Annual potential impact by extrapolating findings of Table 5 to 100,000 motorists using Parker three times a week
Los Angeles Department of Transportation began a pilot program LA Express Park™
in 2012 to reduce congestion and pollution by reducing parking search time (Xerox,
2013) as shown in Figure 12. After a 6 months’ trial, the following benefits were
reported:
Decrease in parking congestion by 10%
Underutilization of parking spaces decreased by 5%
Parking rates decreased by 11% but the parking revenue increased by 2%
A post implementation survey indicated 76% of drivers willing to change their
parking preferences towards less expensive areas.
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Figure 12 Congestion results before and after the pilot program (Xerox, 2013)
Limitations of active PGS
For a public resource, such as on-street parking, an important challenge is reservation
guarantee. Since there are no physical barriers in curb style on-street parking, there is
no real way to reserve an on-street parking spot until arrival (Geng & Cassandras,
2013). This results in the negative synchronization effect of OAPS as observed by
Kokolaki et al. (2012).
Furthermore, as active guidance heavily relies on an interconnected network
technology, it requires implementation of special infrastructure and sufficient
computing resource to serve incoming requests for parking spots (Teng et al., 2008).
These models can be abstracted to fit more dynamic PGS systems. Kokolaki et al.
(2013b) simulated performance of active PGS and discovered a caveat in broadcasting
parking information to all drivers i.e. as the number of drivers with advance parking
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information increases, the penetration rate and/or the competition (parking demand)
also intensifies.
2.5.3 Impact on Parking Search with Pre-Trip Information
Travelers can utilize pre-trip information and make parking plans based on parking
facilities address, cost, access routes and attractions. These systems can be as simple as
a web page providing a static map of parking facilities or a more sophisticated dynamic
map of parking with real time occupancy details (U.S. Department of Transportation,
2007) as shown in Figure 13.
Figure 13 Parking Destination Information Available at ParkMilwaukee.com
(U.S. Department of Transportation, 2007)
Waterson et al. (2001), Thompson et al. (2001), Mei & Tian (2011) and Asakura &
Kashiwadani (1994) have published detailed studies on parking guidance based VMS
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technology as a pre-trip planning tool. Lack of published literature available for parking
guidance models involving on PGS, especially before and after studies was pointed out
by Thompson & Bonsall (1997). More recently, game theoretic models by Kokolaki et
al. (2013a), multi-agent based models by Dell’Orco & Teodorović (2005) and PARKIT
based model developed by (Bonsall & Palmer, 2004) have demonstrated noticeable
effects of advance parking information on driver behaviour and congestion.
2.6 Summary
This chapter analysed the issues surrounding parking in a comprehensively manner.
Topics covered include causes, effects and measures taken to mitigate the parking
problem. The causes of parking problem were identified as follows:
1. An increase in car ownership levels disrupts the balance between parking supply
and demand
2. Information asymmetry i.e. driver’s lack of knowledge and/or experience in
locating vacant parking supply resulting in wasteful parking searches.
3. A parking policy that lacks consideration for driver heterogeneity and socio-
economics yielding counterproductive effects.
The effects of parking problem were identified as follows:
1. Congestion/reduced road capacity.
2. Carbon emissions.
3. Negative effects on road safety.
4. Losses in parking revenue due to ineffective parking land use.
5. Driver frustration and illegal parking.
6. Increased parking prices negatively impacting economic activity.
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A review of parking policies as tool for solving the parking problem was conducted.
The salient features of this review are as follows:
1. It is very difficult to formulate a parking policy that can serve all classes of
commuters in a fair manner. The heterogeneity of drivers is a result of behavioural
parking preferences due to differences in incomes and motivation behind the
commute.
2. Parking policies perform differently based on geography and socio-economic
environment. A policy cannot simply be “plugged-in” because of its success in a
different urban environment.
3. Using pricing as the only tool for controlling parking demand and supply yields
negative economic activity. When prices are increased, drivers change their parking
preferences in terms of location (and walk longer) and therefore still use personal
transport.
4. There is no consensus on whether removing on-street parking or equalizing on-
street parking costs with off-street parking is a solution to the on-street parking
problem. Where there are merits to on-street parking or not is politically charged
topic.
5. There is a need for innovative systems to solve the parking problem.
Driver behaviour was reviewed in detail to make sense of the choices made by drivers
in search of on-street parking. The findings can be summarized as follows:
1. Drivers are most sensitive to parking price and walking distance. They begin their
search by choosing a parking location closest to their destination and keep search
in an outward spiral fashion.
2. Drivers search patterns differ based on personal and trip characteristics. Income,
purpose of trip (work or leisure), rationality, gender and age play an important role
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in driver search. This also applies to the choice between on and off-street search
and walking habits.
3. Past experience and available of pre-trip information plays an important role in how
drivers perform parking search.
The role of transport models related to parking search and associated challenges were
discussed. Finally, PGS were reviewed with prime focus on their comparative
efficiency and limitations. These findings can be summarized as follows:
1. PGS can be categorized into passive and active guidance systems. Passive systems
involve the more prevalent methods of information dissemination, such as VMS,
radio broadcasts or pamphlets. Active guidance is a more recent trend in PGS that
provides more personalized parking information.
2. VMS based PGS are limited in their function due to their physical placement and
lack of information control. Their use is more popular in closed systems (off-street
garages) as compared to on-street parking. Overall, their effectiveness, though
undeniable, is still not as impressive as expected.
3. Active PGS implementations are still rare and there is a lack of published literature
on their effectiveness. Available literature has confirmed noticeable impacts, such
as reduced parking search time, reduced congestion and an increase in revenue.
Active PGS perform extremely well during times of low competition. However, the
synchronization effects become apparent and severe in times of higher completion
when an OAPS algorithm is deployed. A more sophisticated CAPS algorithm
mitigates the synchronization problem by effectively controlling the parking
information based on driver preferences and circumstances. This is based on the
assumption that all drivers are using CAPS.
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4. Pre-trip information and the way it is presented to the driver plays a significant role
in the parking search behaviour.
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CHAPTER 3. SELECTIVE PARKING DISCOVERY ALGORITHM DESIGN
In Chapter 2, I have reviewed parking policy, parking search behaviour and limitations
of existing parking guidance systems. In Chapter 3, I have addressed the limitations of
parking guidance systems by developing a parking guidance system based on the
concept of “selective parking discovery”.
3.1 Background
Extrapolation of the number of vehicles that use parking guidance, published by
Streetline (2013) raises an important question: can we expect an active guidance system
based on opportunistically assisted parking search to increase the utility of all drivers
as the number of drivers using active guidance increases? The significance of this
enquiry also corresponds with the estimated number of in-ground parking sensors
across the globe (Navigant, 2015).
Active parking guidance is a useful tool for drivers to maximize the utility of their
journey. But as the number of drivers using active parking guidance increases, the
utility for drivers is reduced as PGS users reach saturation levels. Kokolaki et al. (2012)
simulated the effects of drivers using active guidance and concluded that during the
times of high competition, active guidance based on opportunistically assisted parking
search (OAPS) performed worse than no guidance at all due to an increased frequency
of synchronization effect. This sheds light on a serious pitfall of this search algorithm
as it broadcasts parking availability information to all drivers and therefore attract them
towards a parking lot, which cannot accommodate all of them. This can be solved by a
centrally assisting parking search (CAPS) algorithm (Kokolaki et al., 2012). CAPS can
ensure cooperation between competing drivers by allowing the algorithm to assign
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parking to drivers instead of drivers parking themselves, given a number of available
parking choices. CAPS must address two challenges:
1. CAPS should have the ability to differentiate between driver classes and
individual trip characteristics, such as maximum walking distance thresholds.
As the competition increases, it should disperse drivers but keep them within
walking distance of their final destinations.
2. In an on-street parking environment, where a parking space cannot be
guaranteed to a driver in advance (due to absence of physical barriers), the
number of drivers that are not using any type of guidance is also a challenging
issue. Drivers not using any type of centrally assisted parking search are
essentially “off-radar” and may occupy free parking spots that are already
assigned to drivers using CAPS.
3.2 Introduction to Selective Discovery Hypothesis
To address the challenges of CAPS presented in Section 3.1 (lack of stated choice and
parking reservation), I have introduced the concept of “selective discovery”.
Selective discovery of parking refers to controlled dissemination of parking occupancy
information. Discovery of available parking depends on a set of rules that govern the
distribution of parking occupancy information among drivers individually based on
their input preferences i.e. duration of stay, parking price and maximum walking
distance from the parking spot to the destination. The motivation behind this concept is
to ensure cooperative behaviour among drivers during times of competition. Based on
driver preferences and real-time data attributes (such as distance from desired parking
location, travel time and current demand), the algorithm selects which driver gets to
discover a particular parking spot to achieve a system optimum.
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Figure 14 A simplified flowchart representation of Selective Discovery
As the number of drivers using parking guidance system approaches saturation levels,
selective dissemination of parking information can be made free of synchronization
effects and reduce unpleasant surprises. This can be achieved by improving upon two
primary areas of existing parking guidance models:
1. Parking occupancy forecasting
2. Parking information processing based on stated-preference approach
3.2.1 Parking occupancy forecasting
Showing real-time parking occupancy to the drivers when they still have time to arrive
at their destinations is not helpful since parking occupancy keeps changing over time.
To tackle this, the parking guidance system encompasses a forecasting module that can
predict the parking occupancy at the time of arrival with useful accuracy (Figure 15).
Driver preferencesIs it suitable to guide driver to
requested parking?Provide parking
guidance
No parking spaces avaiable
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Figure 15 Overview of proposed parking guidance system
This accuracy is described in terms of a mean absolute error3 and a coefficient of
determination 4 . Parking information processor can receive input variables (driver
preferences) and pass it on to the forecasting module. Based on parking occupancy
forecast, the information processor can output pre-trip guidance to the driver. This way,
equipping drivers with pre-trip parking information can influence their behaviour
before they begin the trip. As discussed in sections 2.3 and 2.5.3, pre-trip information
can have a significant impact on a driver’s behaviour. Occupancy trends can be
extracted and expected parking availability can be presented to the driver in a simple
and meaningful manner so they can alter their trip preferences, such as time of departure
and/or choice of location, especially during peak hours (Figure 16).
3 Mean absolute error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. 4 The coefficient of determination, denoted R2, is a number that indicates the proportion of the variance in the dependent variable that is predictable from the independent variable.
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Figure 16 Parking forecast alerts the driver to reconsider parking preferences during peak time
3.2.2 Parking information processing based on stated-preference approach
The CAPS algorithm can be extended to include stated-preference approach to provide
more personalized parking guidance. This way, the algorithm can be designed to
consider a variety of personal factors for parking assignment, such as travel distance,
walking distance, parking time and cost. Parking information processing refers to the
controlled flow of information between a central information processor (server) and the
driver (client) over a persistent 2-way communication channel. The information
processor performs these communications through a standardized application-
programming interface (API). The high-level API will provide an abstraction layer
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allowing developers to create applications for consumer devices like tablets,
smartphones, laptops and in-car navigation systems.
3.3 Selective Discovery: A Queueing Theory Perspective
Queuing theory (QT) is a mathematical method to analyse congestion and delays of
entities waiting in line. Features of physical or virtual systems can be described using
metrics such as input rate of customers, length of queue, number of servers and service
time to model performance of processes. Figure 17 shows a graphical representation of
an abstract queuing model:
Figure 17 Graphical representation of a queuing model
where:
input/output ƛ arrows represent the input and output rate of customers
array of blocks represents the queue
and circles represent servers
Following is an example of describing a queuing model using Kendall’s notation:
A/S/c/K/N/D
where
A is the arrival process
S is the service time distribution
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c is the number of servers
K is the capacity of the queue
N is the size of jobs to be served
and D is the queuing method
An example of a model represented in Kendall’s notation is the M/M/1 model which
assumes a Markovian input and output distributions and a single server. When not
specified, K and N are assumed to be infinity and D is FIFO (first in first out).
3.3.1 Application of QT models to parking
To model parking behaviour using querying theory, we assume a queuing model as
shown in Figure 18:
Figure 18 Parking queuing model
where:
1. ƛ represents the arrivals of vehicle with an inter-arrival time of t
2. switch is the decision process of choosing a queue towards a parking lot
3. μ is the duration of parking (rate of service)
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The generic model shown in Figure 18 can be altered to design 3 variant models for
parking where:
1. Vehicles do not use parking guidance (Figure 19)
2. Vehicles use parking guidance based on OAPS (Figure 20)
3. Vehicles use parking guidance based on SPDA (Figure 21)
Figure 19 No guidance parking model
Figure 20 OAPS parking model
Figure 21 SPDA parking model
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The primary difference between the aforementioned models is the switching
mechanism:
1. For no guidance, the switch assigns the vehicle to a queue at random.
2. For OAPS, the switch assigns the vehicle to a queue which has an available
parking lot (server) without regard for the number of vehicles in queue ahead of
it.
3. For SPDA, the switch routes the vehicle to the least loaded queue. The load of
a queue is calculated by summing up the parking (service) time of each vehicle
in that queue. This allows the switch to estimate and predict parking lot usage
in advance.
3.3.2 QT Parking model simulation and results
Models proposed in section 3.3.1 were developed in SimEvents to evaluate and
compare their performance. SimEvents provides a discrete-event simulation engine and
component library for analysing event-driven systems and optimizing their
performance characteristics.
The vehicles are assumed to have arrival rate of ƛ, inter-arrival time t and service rate
of μ. Theoretically at ƛ = μ, the parking demand and supply is at equilibrium and
vehicles spend ~0 time in queue. Let’s call ƛe and te the arrival rate and inter-arrival at
equilibrium respectively. By altering the value of t in comparison to te, model
performance can be tested for the following scenarios:
1. High flux or equilibrium (t = te) where vehicles are joining the queue for parking
lots at the same interval as they are leaving the parking lot.
2. Low flux (t > te) where vehicles are joining the queue for parking lots slightly
slower than they are leaving the parking lot.
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3. Off-peak hour (t >> te) where vehicles are joining the queue for parking lots a lot
slower than they are leaving the parking lot.
4. Peak hour (t < te) where vehicles are joining the queue for parking lots faster than
they are leaving the parking lot.
5. Over-saturation (t << te) where vehicles are joining the queue for parking lots a lot
faster than they are leaving the parking lot.
The experiment was initialised with a constant service rate (μ) = 1-unit time and number
of parking spots (c) = 4. Service rate has been kept constant to accurately track and
compare queue performance vs. different inter-arrival times.
This follows:
ƛe = μ*c = 1*4 = 4 vehicles/unit time
te = 1/ƛe = 1/4 = 0.25 unit time
t = 0.25 (High Flux or Equilibrium)
t > 0.25 (Low Flux)
t >> 0.25 (Off-peak Hour)
t < 0.25 (Peak hour)
t << 0.25 (Over-saturation)
3.3.2.1 Scenario t = te (High Flux or Equilibrium)
This scenario is initially tested for influx of traffic with a constant value of ƛ and
t=te=0.25 as shown in Figure 22.
Figure 22 Vehicle arrivals (left), arrival rate (middle) and vehicle service times (right) for t=te
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Figure 23 shows the average wait time comparison for no guidance, OAPS and SPDA.
It is overserved that with no guidance, the average wait time in the queue rises steadily.
This is because vehicles choose queues at random and are not always successful at
landing at a parking lot with available spot. In comparison, both OAPS and SPDA
equipped vehicles spend almost no time in the queue (travel times through the queue is
assumed to be zero if a parking lot at the end of it has a parking spot available).
Figure 23 Average wait time comparison for High Flux at t = te with a constant ƛ and t
Figure 24 shows a comparison of average parking utilisation and individual parking lot
comparison between no guidance, OAPS and SPDA. It is observed that parking lots fill
up quickly when vehicles utilise OAPS and SPDA. Individual use of parking lots (P1,
P2, P3 and P4) with no guidance shows that the parking lots are utilised more unevenly
as compared to OAPS and SPDA.
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Figure 24 Parking utilisation comparison High Flux at t = te with a constant ƛ and t
To simulate randomness in traffic, the values of ƛ and t are re-generated using an
exponential distribution as shown in Figure 25 (all remaining scenarios use exponential
distributions).
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Figure 25 ƛ and t based on exponential distribution
This enables a more realistic view of average wait times and parking utilisation as
shown in Figure 26 and Figure 27.
Figure 26 Average wait time comparison for High Flux at t = te where ƛ and t have exponential distributions
It can be observed in Figure 26 that an exponential distribution of ƛ and t has introduced
wait times for OAPS and SPDA as a result of its non-constant values. OAPS performs
better than no guidance whereas SPDA outperforms no guidance and OAPS. This is
because OAPS ignores queue load and only focuses on the current availability of
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parking at any given time. SPDA demonstrates better resistance in the face of
unpredictability of parking lot usage by prioritising queue load over current availability
of parking lot.
Figure 27 shows that parking SPDA utilises parking more efficiently than both OAPS
and no guidance. Parking use for the SPDA model shows that parking lots fill up faster
than OAPS as less time is wasted on parking search.
Figure 27 Parking utilisation comparison for High Flux at t = te where ƛ and t have exponential distributions
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3.3.2.2 Scenario: t > te (Low Flux)
This scenario is for influx of traffic with exponential distributions ƛ and t=0.26 and
te=0.25. Average weight time shows steady rise for all three models however SPDA
shows significantly less wait times over the course of simulation (Figure 28).
Figure 28 Average wait times for t > te
Parking utilisation plot in Figure 29 shows that SPDA fills up parking lots faster than
no guidance and OAPS. While OAPS fills up parking lots more evenly, the average
parking lot utilisation is not much different no guidance past 400 unit simulation time.
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Figure 29 Parking utilisation for t > te
3.3.2.3 Scenario: t >> te (Off-peak hour)
This scenario is for influx of traffic with exponential distributions ƛ and t=0.30 and
te=0.25. Average wait time comparison in Figure 30 shows that SPDA results in the
least amount of wait times as compared to the rest of the models. OAPS, in contrast to
wait times found in scenario: t > te, shows significant improvement as the inter-arrival
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time between vehicles is much greater and therefore increases utility of using OAPS
over no guidance.
Figure 30 Average wait time for t >> te
Parking utilisation plot shows that OAPS and SPDA utilises parking more efficiently
as compared to no guidance (Figure 31). While SPDA does not consistently out-
perform OAPS in average parking utilisation, it manages to utilise individual parking
lots more evenly as compared to OAPS.
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Figure 31 Parking utilisation for t >> te
3.3.2.4 Scenario: t < te (Peak hour)
This scenario is for influx of traffic with exponential distributions ƛ and t=0.24 and
te=0.25. Average wait time plot shows that SPDA results in the least amount of wait
time as compared to no guidance and OAPS (Figure 32).
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Figure 32 Average wait time for t < te
Parking utilisation plot (Figure 33) shows that parking utilisation efficiency begins to
converge for all models later in the simulation with SPDA performing slightly better as
compared to the other two models. This is also reflected in individual parking lot
utilisation plots where SPDA performs marginally better as compared to OAPS at the
start of the simulation and parking lots filling up more quickly and evenly as compared
to the competition.
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Figure 33 Parking utilisation for t < te
3.3.2.5 Scenario: t << te (Over-saturation)
This scenario is for influx of traffic with exponential distributions ƛ and t=0.20 and
te=0.25. Average wait time plot shows that SPDA results in less average wait times as
compared to the competition (Figure 34). While OAPS outperforms no guidance at the
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beginning of the simulation, its performance converges with no guidance at 2500 unit
simulation time. Past this point, OAPS performs worse than no guidance. This is
because the probability of available parking spot based on real-time availability of
parking lot becomes lower than a random search. OAPS assigns vehicle a queue based
on current availability however the chance of other vehicles already in route to that
parking lot is much higher. This is called the synchronisation effect where vehicles
travel towards a vacant parking lot but fail to occupy it as there are other vehicles also
using OAPS ahead of it.
Figure 34 Average wait time for t << te
Parking utilisation plots show that SPDA performs marginally better than the
competition overall whereas OAPS and no guidance converge overtime. Individual
parking lot utilisation shows that OAPS and SPDA performs similar at the beginning
of the simulation however SPDA fills up parking lots more quickly and evenly past 300
unit simulation time.
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Figure 35 Parking utilisation for t << te
From the QT simulations conducted, it is concluded that SPDA outperforms the
competition in all 5 scenarios presented in section 3.3.2 in the areas of parking search
time and parking utilisation. While OAPS performs better than no guidance in most
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situations, it results in detrimental effects at higher penetration rates for over-saturated
traffic conditions and performs worse than no guidance.
3.4 Development of Selective Parking Discovery Algorithm Logic
This section discusses development of SPDA logic based on the inferences made in
section 3.3. The main goal is to design a parking guidance algorithm that factors in
vehicle switching towards parking lots using the least loaded queue methodology
(parking occupancy forecasting) and vehicle collaboration (central information
processing) to eliminate the synchronisation effect at high penetration rate of guided
parking search.
3.4.1 Parking Occupancy Forecasting Methodology
In Section 3.2.2, I have presented the concept of pre-trip parking occupancy
information as a core component of SDPA. In order to provide pre-trip parking
occupancy forecasts to drivers, a methodology required to conduct quantitative analysis
of on-street parking patterns for forecasting purposes was formulated. This
methodology is described in the following sections:
I. Identification of Goals
The goals of the forecasting parking are defined before data collection and analysis. It
is important to interview people who will use these forecasts in order to fully understand
their requirements. This way, the data analyst establishes a clear vision of what is
required of the forecast. The interviewee category includes parking policy makers,
drivers and business owners in proximity of the parking locations. Examples of what
can be forecasted include:
Parking occupancy
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Rate of arrivals and departures
Parking duration
Violations
Revenue/social surplus
II. Data Collection
Quantitative forecasting requires that historic data is available. It also is expected that
aspects of historic data patterns will repeat in future. The accuracy of forecast also
depends on the amount of the historic data available and the rate at which it was
captured or sampled. A popular method of collecting parking occupancy data is by
using in-ground parking sensors. These sensors record time when a vehicle arrives and
departs a parking space. Other methods of parking data collection include:
Field surveys
Cameras (fixed and aerial)
Simulation studies
Indirectly by measuring revenue
Data spread across a wide span of time ensures encapsulation of recurring medium and
long-term patterns (school holidays, public holidays, Christmas, seasons etc.). Short-
term dynamic data attributes, equality critical to the analysis, are also collected (local
events and weather conditions etc.).
III. Data Validation
Once data is collected, it is important to analyse the validity of the data. How much
data is available and can this data be trusted? Furthermore, the accumulated data may
not be available in a form suitable for analysis. Data can be spread across multiple files
or formats. The data validation step ensures a coherent and organized dataset, which
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can be efficiently consumed by statistical software. Other examples of data validation
include building database schemas and removal of unrelated data to reduce the dataset
footprint.
IV. Preliminary Analysis
The first task in parking data analysis is plotting the data as time series to visualize
patterns, the relationship between different variables and unusual observations. The
time series is also decomposed to identify trends, seasons and cycles.
For instance, when analysing parking occupancy, a trend exists when there is a long-
term increase or decrease in parking occupancy. A seasonal pattern exists when
occupancy is influenced by seasonal factors (e.g., the quarter of the year, the month, or
day of the week etc.). Seasonality is always of a fixed and known period. A cyclic
pattern exists when occupancy exhibit rises and falls that are not of fixed period.
The errors in parking data are also identified at this step. Any erratic and unexpected
patterns in data that cannot be explained by the attributes (e.g. sensor failure) are
corrected. If these errors are not corrected, the resulting forecasts will not be accurate.
V. Attribute Selection & Correlation
Parking trends rely on a variety factors. They can be called variables. Examples of these
variables include (in no particular order):
1. Location.
2. Price
3. Capacity.
4. Time of the day.
5. Day of the week.
6. Day of the month.
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7. Month of the year.
8. Weather conditions.
9. Holidays (School holidays, Christmas, etc.).
10. Local events.
Using a large number of attributes does not always guarantee a more accurate forecast.
Depending on what data modelling technique is used, removing attributes from the
dataset may increase or decrease the accuracy of the forecast.
VI. Forecast Modelling
Different forecasting models (e.g. regression trees and neural networks) can be tested,
evaluated and analysed to compare prediction errors in parking occupancy. Forecasting
errors can be identified using residual analysis5. Residuals are plotted and analysed for
forecast accuracy and residual autocorrelation. Residuals demonstrating
autocorrelation suggests that data patterns were not fully captured by the forecasting
model. Models can be statistically tested against errors by calculating Mean Absolute
Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and
Coefficient of Determination (R2). It is also useful to plot 3D charts of forecast value
vs. actual value against time to conduct a temporal analysis of residuals to further
investigate causality of residuals.
3.4.2 Information Processing Using Selective Discovery Algorithm
As discussed earlier, the selective parking discovery algorithm framework (SPDA)
logic is driven by the central information processor (CIP). CIP relies on a range of
5 Residual is the difference between the actual value and forecasted value
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parking metrics to initialize and trigger guidance instructions. Parking metrics are a
collection of data attributes that drive the logical flow of communication in the
proposed information processor. These metrics have been defined in Table 7 and
visualised in Figure 36.
Assisted Search The search for parking by a driver using SPDA
Unassisted Search The search for parking by a driver not using any type of guidance
Driver Location Value pair that represents the location of the driver.
Destination Location Value pair that represents the location of the destination.
Parking Duration Intended length of stay at the parking spot.
Travel Time Expected time required traveling from the driver location to the parking location.
Walking Distance The maximum distance the driver is willing to walk between the suggested parking location to the actual destination.
Target Parking Lot(s) Locations (as latitude and longitude value pair) of parking lots that fall in the walking distance radius from the destination.
Parking Lot Capacity The total number of parking spots available in the target parking lot.
Parking Lot Spaces Occupied The number of occupied parking spaces in a particular target parking lot.
Assisted En-route Vehicle Incoming vehicle assigned to the target parking lot by the guidance system.
Parking Lot Demand The number of times a target parking lot was requested (or pinged) recently by the guidance system.
Parking Lot Occupancy Trend Whether the parking occupancy for a parking lot is going up or down.
Table 7 Parking metric definitions
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Figure 36 Visual representation of parking metrics
The aim of SPDA is to maximize the utility of driver. The utility function of the driver
(based on section 2.3 and 2.4) can be represented as follows:
U = f( dv, ts, dw, cm, cnm, e )
Where, for any driver searching for parking:
‐ U is the overall utility function of the driver
‐ dv is total distance travelled in search of the parking
‐ ts is total time spent in searching for the parking
‐ dw is walking distance from the parking lot to the final destination
‐ cm is out of pocket cost of parking (parking tariff, fuel consumption)
‐ cnm is non-monetary cost of parking search (wasted time and perceived
inconvenience)
‐ e is the user experience index which depends on the accuracy of parking guidance
(positive, negative or neutral)
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3.4.3 Central Information Processor (CIP) Design
CIP is designed to maximize driver utility competing for parking resource by achieving
system optimum. When a driver requests parking, CIP takes driver preferences as input
variables and process them against a parking guidance algorithm.
The logical flow of information is as follows:
1. Driver requests parking by providing the following data:
a. Current location
b. Destination location
c. Maximum walking distance driver is willing to walk from its final
destination (stated-preference)
d. Parking duration
e. Maximum parking cost the driver is willing to pay (considered only in case
of a dynamic parking pricing scheme is in place)
2. CIP hands over all metrics to the forecasting module (pre-trip journey evaluation).
The forecasting module computes the predicted occupancy for the requested
destination radius (defined by the maximum walking distance) at the time of arrival
using historic trends.
a. If the forecast suggests high saturation levels or peak hours, the driver is
warned and advised to reconsider mode choice (use public transport, park
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and ride or reschedule trip). The driver, at this point can either choose not
to drive or to continue with the guidance process.
b. If the forecast levels suggest available parking spaces based on arrival time,
continue with usual operation of parking guidance.
3. CIP receives driver input data and performs two functions:
a. Save driver search preferences into the database
b. Register search attempt (increment if a previous search was made)
4. CIP finds out target parking lots within the walking distance radius using a static
parking location and tariff database. The tariff database contains information about
parking rules (e.g. allowed parking times and restrictions). Once these parking lots
are identified, travel times to each parking lot is calculated.
5. CIP checks if the driver can park at target parking lot for the desired parking period
considering the time of arrival (calculated using travel time).
6. If parking rules suggest that parking is not allowed or at full expected capacity at
arrival time, CIP outputs this information back to the driver and sets the driver status
as waiting in queue.
7. If parking at the selected parking lots is allowed at arrival time for the requested
duration, parking lot with the highest number of free spaces is selected for parking
guidance. If the available parking spaces are equal among shortlisted parking lots,
the parking lot closest to the final destination is selected.
8. Once parking lot is allocated to the driver, CIP increments the en-route vehicles
index for this parking lot. En-route index helps CIP keep track of incoming vehicles
for that parking lot so it can keep track of previous assignments. Keeping a record
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of en-route vehicles in the database can be thought of as a form of virtual reservation
system.
9. CIP then passes the allocated parking lot ID to the journey evaluation controller
(JEC). JEC retrieves data of vehicles that have already been assigned to the given
parking lot ID. This is to check if the latest parking assignment has affected the
parking journeys of incoming vehicles. In such a case, the en-route vehicle which
is farthest from this parking lot ID is cancelled and set for re-evaluation of parking
guidance. This is based on weighted queuing algorithm which priorities vehicles
with shorter travel times over vehicles that arrive later. This is because of the
following reasons:
a. Compared to longer travel times, shorter travel times are less prone to
unexpected outcomes, such as arrival of “off-radar” non-users.
b. It is essential to disseminate vehicles in queue cruising for parking close to
their destination and reduce localised congestion.
Once a vehicle is parked, CIP runs a routine as follows:
1. If an assisted vehicle is parked, then increment parking occupancy level for the
concerned parking.
2. If a non-assisted vehicle was parked, increment the parking occupancy level and
alert the JEC for re-evaluation of parking guidance of all en-route vehicles.
This differentiation is important since not all vehicles in search of parking are using
parking guidance. Therefore, if an unexpected parking (arrival or departure) event is
observed, CIP must notify JEC to check if the unanticipated change in occupancy
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required re-evaluating of guidance for en-route vehicles. Similarly, JEC is notified if a
journey is aborted midway.
Figure 37 Simplified workflow of SPDA. For a detailed functional diagram, refer to Appendix A.4
3.5 Simulation Design
A simulation framework was designed to test and validate performance of SPDA. The
framework allows to couple traffic simulation, parking information systems, and end-
user (driver) decisions. The following sections describe the framework, the simulation
area, the base simulation, and the full-scale simulation testing the parking algorithm.
Parking Search Request by vehicle
Get vehicle parameters
Get travel time to parking lots within walking distance of
vehicle
For each parking lot, check Parking Lot tariff at arrival time
(parking validity)
For each valid parking lot, get expected occupancy at arrival
time
Parking spaces available?
Add vehicle to queue
Assign vehicle to parking lot with highest number of free
spaces
Run JEC subroutine
NO
YES
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The simulation framework consists of an event-based traffic simulator which provides
a base simulation model. All simulation elements are held in a common simulation
database for on-line monitoring, and off-line analysis (Figure 38).
Traveller information of any kind can be added to the simulation by providing a
Traveller Information API interface to the simulator. The API interface is used to send
a selection of events from the simulator to any external control logic.
Figure 38 Simulation Framework with deterministic demand model
The parking guidance algorithm receives information an API and can manipulate the
ongoing simulation by accessing a shared simulation database. In this way, the API
interaction can be kept to a minimum to preserve network resources. During data
manipulations in the shared simulation database, the traffic simulator is slowed
artificially to 10-times real time speed to ensure computational integrity.
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The simulation area is composed of Brisbane CBD and surrounding major arterial
network. This area provides a playing field for vehicles that depart from 19 origins and
arrive at 34 destinations and generate travel demand involving 22 parking lots as shown
in Figure 39.
Figure 39 Simulation Area
The aim of the simulation is not to represent the vehicles' journey and parking
movements, but to capture the process of trying to find parking within a maximum
walking distance of desired destination. Vehicles are routed through the network based
on a minimum travel time identified at the time of departure. The vehicle movements
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in this event-oriented simulation is based on the link performance function by the
Bureau of Public Roads (BPR):
Where:
ta = free flow travel time on link a per unit of time
va = volume of traffic on link a per unit of time (more accurately: flow
attempting to use link a).
ca = capacity of link a per unit of time
Sa(va) is the average travel time for a vehicle on link a
Simulation is carried out for a total of 4 hours where the following general assumptions
are made:
Vehicles travel on fastest route between origin and destination.
Vehicle travel at maximum allowed speed or link speed based on traffic
situation.
Vehicles choose closest parking lot available within a pre-defined walking
distance.
Vehicles park instantaneous without any further delays due to the parking
manoeuvre
Vehicles follow advice if directed by the parking algorithm (including
abandoning trip).
Vehicles consider any parking available when search time has reached 15
minutes.
During the course of a vehicle’s journey, it is marked with one of the following three
statuses:
1. Arrived; when vehicle is within 2 km of its destination. This is assumed to be
the “accepted” walking distance from the final destination.
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2. Searching for parking; when vehicle starts looking for a vacant parking spot
within the walking distance.
3. Parked; when vehicle arrives at a vacant parking lot.
After the vehicle has parked for the requested duration, it is assigned a random point as
the next destination where it disappears (representing end of journey). For each journey,
the following vehicle variables are captured:
Origin, Destination (one way)
Start time (simulation)
Timestamp for ‘arrived’
Timestamp for ‘search for parking’
Timestamp for ‘parked’
Number of parking lots recommended (equipped vehicles only)
Distance to final destination from carpark
Flag for abandoned trip
3.5.1 Baseline Scenarios:
The baseline simulation scenarios are run by varying the following parameters:
Parking availability patterns:
o Low availability/High fluctuation (LA/HF)
o Medium availability/Low fluctuation (MA/LF)
o Medium availability/High fluctuation (MA/HF)
Parking demand:
o Low demand where every car can be served (LD)
o High demand where most cars can be served (HD)
o Very high demand where parking is scare and not every vehicle on the
network can be accommodated in a parking lot (VHD)
Baseline traffic
o Low volume (LV)
o Medium volume (MV)
o High volume (HV)
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No car is parking guidance
The baseline scenarios are simulated 10 times to provide input for the parking
availability prediction and as a benchmark to test the effect of the parking algorithm on
the overall network performance and parking search time.
3.5.2 Parking Scenarios:
To simulate efficiency of parking guidance, the following simulation parameters are
introduced:
Percentage of vehicles equipped with parking guidance to consider effects
of system penetration
Reliability of parking lot information to consider system failures or delay of
information
Reliability of travel time prediction to consider errors in travel time prediction
due to traffic incidents
Reliability of parking lot availability prediction to consider days that do not
align with ‘usual traffic patterns
For each parking scenario, variance is introduced in parking guidance penetration rates
and reliability of parking guidance information to reflect real-world scenarios and
measure its sensitivity to fluctuation in traffic conditions.
3.6 Summary
In chapter 3, I have presented an overview of SPDA and its building blocks. Firstly, the
following active guidance challenges based on OAPS and CAPS were noted:
1. As penetration rate for OAPS based active guidance increases, the guidance
information becomes less useful due to increasing synchronisation effects.
2. CAPS should be able to process parking preferences factors such as walking
distance and can potentially park vehicles too far from the destination.
3. CAPS should have the intelligence to make parking assignments during times of
high competition with useful accuracy.
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In the light of challenges presented, a new concept called “selective parking discovery”
is introduced that solves the challenges faced by CAPS by using stated preference
approach and parking occupancy forecasting.
To validate stated-preference approach and selective discovery hypothesis, a simulation
framework was developed. To forecast parking occupancy, a mythology was developed
to conduct predictive analytics. Implementation of the simulation framework and
parking forecasting methodology is presented in Chapter 4.
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CHAPTER 4. FORECASTING CASE STUDY & SIMULATION
This chapter outlines implementation of SPDA using occupancy forecasting case study
and SPDA simulation, as described in chapter 3. The results have been presented and
discussed at the end of each sub-section.
4.1 Parking Occupancy Forecasting Case Study
Using the forecasting methodology presented in Section 3.3.1, a case study was
conducted to forecast parking occupancy in Melbourne, Australia. The aim of the study
was to study parking patterns and assess feasibility of a pre-trip parking guidance
system.
To conduct predictive analysis, Melbourne City Council provided parking occupancy
data under the open data platform6. The City Council of Melbourne has installed in-
ground parking bay sensors in most CBD parking bays. These sensors record parking
events, which is when a vehicle arrives and when it departs. Each event also includes
the parking restriction for the bay and whether the vehicle has overstayed that
restriction. The following tables specify details of the Melbourne parking events dataset
that were used for purposes of this study.
Data attribute Definition
Street Name Street upon which the vehicle is parked.
Street 1 Closest Intersecting street with the street parked on. Ideally the next one in front of the parked vehicle.
6 Melbourne Data is the City of Melbourne’s open data platform which provides public access to a variety of council data.
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Street 2 Closest Intersecting street with the street parked on. Ideally the next one behind the parked vehicle.
Street The plate marker physically present at the parking location.
Arrival Time Date & Time that the sensor detected a vehicle located over it.
Departure Time Date & Time that the sensor detected a vehicle no longer located over it.
Duration of Parking Event Time difference between arrival and departure events (measured in seconds).
Device ID Serial number of the in-ground sensor.
Table 8 Parking Events Dataset Attributes
Size of dataset 3 GB
Total number of parking events recorded 13,493,360
Streets 327
Number of installed sensors 6,690
Data start date 1/1/2014
Data end date 1/1/2015
Table 9 Parking Dataset Specifications
4.1.1 Data Preparation & Development of Analytical Tools
To better analyse and understand the data, the complete dataset was downloaded onto
a local MYSQL server. This was to ensure that any HTTP request/response lags were
omitted as a result of direct integration with the Melbourne Data API.
The next task was to calculate and visualize occupancy of a street segment at any
particular time range for preliminary analysis. This was not straightforward since the
database recorded only the parking events as interrupt data (as opposed to polled data
over regular intervals). To calculate parking occupancy over irregular intervals of time
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on a particular street, a sampling algorithm was developed to create a Parking Data
Visualizer for data mining (PDV).
Figure 40 Parking Data Visualizer
PDV was built to create comparative reports to visualize parking occupancy trends for
a particular day of the week (Mon, Tue, Wed, etc.) over a period of time. It also allows
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plotting of weeks as multiple data series to identify and compare weekly trends in
occupancy. It also provisions a data table to search, filter and sort occupancy data if
needed. Upon every run, PDV exports a comma separated value (CSV) file to disk.
This CSV file can be imported into statistical software for further processing such as
Excel, MATLAB, SPSS, etc. for further processing of the time series data.
PDV’s comparative reporting feature was used to quickly identify preliminary trends
and seasons in parking occupancy. As seen in Figure 18, the occupancy trend can be
recognized visually (judgmental analysis).
Figure 41 Comparative Report of five consecutive Saturdays for a segment of William Street. Time sampled every 15 minutes.
The exported data from PDV was imported into RStudio for time series decomposition
and analysis. RStudio is an integrated development environment (IDE) for the R
programming language for statistical computing and graphics.
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4.1.2 Statistical Evaluation of Seasonality in Parking Occupancy
This section provides a statistical analysis of trends and seasons occurring in Melbourne
parking data. The analysis was conducted using R (a programming language and
software environment for statistical computing and graphics supported by the R
Foundation for Statistical Computing.)
4.1.2.1 Seasonality detection by exploratory analysis
Similar to other aspects of transportation, parking activity is also related to socio-
economic activity and therefore patterns are expected to exist on a daily basis.
Moreover, patterns specific to non-working days (Saturday and Sunday) are also
expected. Seasonality of this nature can be exposed using daily and weekly seasonal
plots. Clustering of patterns in a weekly seasonal plot can be seen in Figure 43.
Figure 42 Weekly Seasonal plot for parking occupancy
Weekdays are observed to follow a consistent trend with gradual rise and fall in
occupancy with a sudden decline and peak in the evening. A distinguishable pattern is
observed for Sunday where shows lack of a dramatic peak during at evening time. To
better illustrate this clustering, Figure 43 shows a colour coded daily seasonal plot.
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Figure 43 Daily Seasonal Plot of parking occupancy
In the daily seasonal plot, the distinguishable pattern of Sunday (cyan) can be easily
observed in contrast to the rest of the week. This plot also exposes two slightly unique
re-occurring patterns for Saturday (blue) and Monday (magenta).
4.1.2.2 Seasonality Detection Using FFT
The FFT (Fast Fourier transform) of a signal can be used to decompose a signal into all
the possible frequencies that make it up (Pollock et al., 1999). Frequencies with stronger
harmonics that contribute to the signal in a meaningfully can be examined by
calculating its spectral density using a periodogram (a common tool for examining the
amplitude vs. frequency characteristics of FIR filters and window functions). Figure 44
shows periodogram for the raw parking occupancy signal.
Figure 44 Periodogram for parking occupancy signal
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A significant spectral density spike can be observed at frequency 2.083333e-02
corresponds to 1/f = 1/2.083333e-02 = 48 in the time domain which is equal to 24
hours/1 day (since samples are taken at half-hourly intervals). Table 10 shows the top
3 frequencies with respect to their spectral densities in descending order.
Frequency (f) Spectral Density Time Period (1/f)
2.083333e-02 7545086.29 48.0 (24 hours) 2.088889e-02 607507.21 47.872340 (~24 hours) 6.250000e-02 408610.21 16.0 (~8am)
Table 10 Top spectral densities of frequencies present in the parking occupancy signal
Reoccurrence of daily frequency points to a strong daily seasonality. Spectral density
peaks at time at ~16 points to the reoccurring pattern seen in Figure 43 where parking
activity begins daily at 8am (data is sampled at half-hourly intervals therefore 16/2 =
8).
In order to uncover the other contributing underlying frequencies, noise in the signal
reduced using Gaussian smoothing. Periodogram and frequencies in descending order
of their spectral density of the smoothed signal are presented in Table 11.
Figure 45 Periodogram for smoothed parking occupancy signal
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Frequency (f) Spectral Density Time Period (1/f)
0.0207407407 57106.9573 48.21429 (~24 hours) 0.0029629630 9370.8472 337.50000 (~1 week) 0.0211111111 9311.0631 47.36842 (~24 hours)
Table 11 Top spectral densities of frequencies present in the parking smoothed occupancy signal
While daily seasonality remains dominant in the smoothed signal as well, the weekly
seasonality has also become apparent in this case at 0.0029629630 Hz which is
equivalent to 337.5 in the time domain i.e. approximately 1 week (48x7 = 336 = 1 week
based on half-hourly data). These findings are consistent with our seasonal plots which
also demonstrate a strong daily and weekly season.
4.1.2.3 Seasonality detection using ACF and PACF
ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) are
measures of association between current and past series values. ACF is the correlation
of a signal with itself at different points in time while PACF gives the partial correlation
of a time series with its own lagged values, controlling for the values of the time series
at all shorter lags. It contrasts with the autocorrelation function, which does not control
for other lags. ACF and PACF plots for parking occupancy data is shown in Figure 46
and Figure 47.
Figure 46 ACF plot of parking occupancy data
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Figure 47 PACF plot of parking occupancy data
The ACF plot shows a sinusoidal pattern indicative of a strong daily season. Moreover,
the burst at lag 7 suggest a weekly seasonality. This is more evident in the PACF plot
where spikes going beyond the confidence interval (thereby rejecting null hypothesis)
can be observed at lag multiples of 7 therefore indicating a strong weekly seasonality.
This finding is consistent with seasonal plots as part of our exploratory analysis.
Moreover, the spikes between lags 1 through 6 decay out quickly over the proceeding
cycles indicating correlation between parking occupancy values that are exactly 24
hours apart for the first cycle and not much afterwards.
4.1.2.4 Verification of seasonal component using TBATS model
TBATS (Exponential smoothing state space model with Box-Cox transformation,
ARMA errors, Trend and Seasonal components) is a statistical model that can be used
to determine if a seasonal pattern is present in time series data. If the algorithm applies
a seasonal model to the time series data, it means that a seasonal component is present.
This test was conducted for both daily and weekly seasonality. TBATS model
confirmed presence of both daily (seasonal period = 48) and weekly (seasonal period =
336) seasonality. The R code and console response for this test are shown below:
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fit<-tbats(parking_ts, seasonal.periods = c(48))
seasonal<-!is.null(fit$seasonal)
seasonal [1] TRUE
fit<-tbats(parking_ts, seasonal.periods = c(336))
seasonal<-!is.null(fit$seasonal)
seasonal [1] TRUE
Statistical evaluation of trends, seasons and cycles observed revealed that the parking
occupancy patterns are not entirely random. Hence, this data can be used for forecasting
purposes. Parking cost, as a static variable, may not prove to be vital for forecasting, as
predictive models will be built on a street-to-street basis (as each on-street lot reveal
characteristic patterns). This is because every parking lot has its own ecosystem driven
by a unique set of features such as location specific facilities, parking tariff, recurring
local events etc.
4.1.3 Forecasting Parking Occupancy
Experiments based on Melbourne CBD parking data were conducted to evaluate the
feasibility and effectiveness of parking occupancy forecasting using machine learning
technology.
4.1.3.1 Data Preparation Feature Engineering
To conduct these experiments, a full year of parking occupancy data at half hour sample
frequency was compiled for street segments. Besides occupancy data, the following
weather data was also included in the dataset:
Temperature
Humidity
Wind speed
Rain
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Feature engineering7 was utilized to add more attributes to data set by introducing
lagged variables8 such as occupancy:
at the same time, last week
at the same time, last fortnight
at the same time, last month.
The time stamp data column was split up into individual data columns to capture
periodicity of occupancy. A working day column was also added to capture effects of
Saturday, Sunday and public holidays on parking trends. A summary of the complete
dataset is outlined in Table 12.
Data column Range/Units Type
Day of the month 1 – 31 Numeric
Month of the year 1 – 12 Numeric
Day of the week 1 – 7 (1=Mon, 2=Tues …) Numeric
Hour 0 – 23 (24 hour format) Numeric
Minute 0 or 30 (30 min intervals) Numeric
Working day 1=Yes, 0 = No Numeric
Temperature Celsius Numeric
Wind Km/hour Numeric
Humidity Percent Numeric
Rain 1 = Rain, 0 = No Rain Numeric
Same time occupancy yesterday 0 – Max capacity Numeric
Same time occupancy previous week 0 – Max capacity Numeric
7 Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. 8 Lagged variables are used in a regression equation to predict values of a dependent variable based on both the current values of an explanatory variable and the past period values of the variable to capture dynamic effects.
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Same time occupancy previous fortnight 0 – Max capacity Numeric
Same time occupancy previous month 0 – Max capacity Numeric
Present Occupancy 0 – Max capacity Numeric
Table 12 Dataset details for a given street segment
4.1.3.2 Experiment Setup
Microsoft Azure Machine Learning Studio was used to conduct forecasting
experiments as it provides a visual node-based environment for performing predictive
analytics. The setup consists of data source, selection of data features (i.e. input data
attributes), splitting data into training and testing sets, tuning of regression algorithm
based on input data columns, scoring and evaluation of the trained model.
Figure 48 Experiment layout in Azure Machine Learning Studio
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To train the predictive models, two supervised machine learning9 algorithms were
evaluated side by side (Figure 49), namely:
Boosted Decision Tree Regression: a machine learning algorithm which
produces a prediction model in the form of an ensemble of weak prediction
models, typically decision trees.
Neural Network Regression: a machine learning algorithm modelled on the
human brain and nervous system.
These algorithms were chosen since they show excellent accuracy for regression and
have fast training times (Azure, 2016).
Figure 49 (a) Boosted Tree Regression Model (b) Neural Network
Input Feature Selection
To find best input features for highest model accuracy, Permutation Feature Importance
(PFI) module was used. PFI scores features (input data columns) based on how much
the performance of the model based on the dataset changes when the feature values are
9 Supervised learning is the machine learning task of inferring a function from labelled training data.
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randomly shuffled. PFI works by randomly changing the values of each feature column,
one column at a time, and then evaluating the input model. Important features are
usually more sensitive to the shuffling process, and will thus result in higher importance
scores (Figure 50a). The scores that the module returns represent the change in the
performance of a trained model, after permutation (Azure, 2016).
Fine tuning of algorithms
To find the best combination of model input features for highest accuracy, mean
absolute error (MAE) and coefficient of determination (R2), the Tune Model
Hyperparameters (TMH) module was used (Figure 50b). TMH performs a parameter
sweep on a model to determine the optimum parameter settings with the aim of finding
the best combination (Azure, 2016).
Figure 50 (a) PFI workflow (b) TMH workflow
Select an input feature
Predictions improving?
Shuffle values
Assign positive score to input feature
YES
NOEnd of
permutations?
YES
Assign negative scoreto input feature
NO
Select a modelparameter
Predictions improving?
Shuffle parametervalues
YES
NO
Optimum achieved?
YES
Set parameter value
NO
(a) (b)
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4.1.4 Forecasting Experiment Results
An experiment was conducted for multiple street segments to evaluate performance of
each regression algorithm. To demonstrate this methodology, the following results
were collected from a forecasting experiment conducted for a segment between
Elizabeth street and Swanston street on A’Beckett Street.
4.1.4.1 Results of Boosted Decision Tree Regression Algorithm
TMH module calculated the optimal settings for Boosted Decision Tree Regression
with highest value of R2 at 0.8555 (Figure 51).
Figure 51 Output of TMH module
PFI scores indicate that month and rain have no effect on predictive model accuracy.
Humidity and wind show negative scores which indicate that these attributes are
decreasing accuracy of the predictive model (Figure 52 Left). After removing humidity
and wind, R2 was increased from 0.855 to 0.859 and MAE reduced from 1.164 to 1.140
with a high frequency of very small errors (<0.88) in occupancy prediction (Figure 52
Right).
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Figure 52 (Left) Output of PFI module (Right) Model evaluation and error histogram
Figure 53 shows a comparison of actual and predicted parking occupancy values.
Figure 53 Comparison of actual (x-axis) vs. scored labels/predicted values(y-axis). The points along black dotted line represent predictions with least amount of error.
Actual Occupancy
Pre
dict
ed O
ccup
anc
y
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4.1.4.2 Results of Neural Net Regression Algorithm
TMH module calculated the optimal settings for Neural Network Regression with
highest value of R2 at 0.829 (Figure 54). PFI scores indicate that month and rain have
no effect on predictive model accuracy. Humidity and wind show negative scores which
indicate that these attributes are decreasing accuracy of the predictive model (Figure 55
Left). After removing month and day, the coefficient of determination (R2) was
increased from 0.829 to 0.845 and reduced MAE from 1.233 to 1.179 with a high
frequency of very small errors (<1) in occupancy prediction (Figure 55 Right).
Figure 54 Output of Tune Hyper Parameters module showing optimal settings for Neural Net Regression for highest value of Coefficient of Determination (R2)
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Figure 56 shows a comparison of actual and predicted parking occupancy values. It can
be observed that the model performs similar to Boosted Decision Tree Regression with
best predictions at times of very low and high occupancy periods. However, residuals
observed here are much larger in Neural Networks due to a bigger MAE and smaller
R2 value.
Figure 55 (Left) Output of PFI (Right) Model evaluation and error histogram
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Figure 56 Comparison of actual (x-axis) vs. scored labels/predicted values(y-axis). The points along black dotted line represent predictions with least amount of error.
4.1.5 Conclusion
Based on additional experiments (see Appendix) and those conducted section 4.2.4, the
following observations were made:
1. For all street segments analysed, Boosted Decision Tree Regression algorithm
performed better than Neural Networks in terms of training time, higher values
of R2 and lower values of MAE.
2. Weather had the least amount of impact on the accuracy of forecasts, with
humidity and temperature having slightly more influence than the rest of
weather data features. While weather played no significant role in the model
accuracy for Melbourne, its impact may be more noticeable for other regions
based on mode options, purpose of trip and characteristics of travellers (Rudloff
et al., 2014).
Actual Occupancy
Pre
dict
ed O
ccup
ancy
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3. Lagged occupancy variables had the biggest influence on occupancy prediction.
Notably “occupancy last week” was the most important predictor among the
rest of the lagged variables.
4. Input data features impact every street differently, therefore fragmented
predictive models should be built for parking zones with unique characteristics.
This can be attributed to the unique features of location (e.g. proximity to
offices, shops, parks etc.) and therefore resulting in unique occupancy patterns
(Leu & Zhu, 2014).
In all experiments, Boosted Decision Tree Regression algorithm achieved a value of R2
between 0.8 and 0.95 and MAE between 1.1 and 1.25. Therefore, it is concluded that
Boosted Decision Tree Regression is a more accurate and efficient algorithm for
predicting parking occupancy as compared to Neural Networks and suitable for
application in parking occupancy prediction.
4.2 SPDA Simulation and Results
4.2.1 System Architecture
SPDA was implemented in the PHP/SQL programming languages and hosted on a Mac
OSX based Apache server. To enable communications between the simulator and
algorithm, a representational state transfer (RESTful) based JSON API was developed
as a wrapper (Figure 57). The advantage of using such as an API is that any external
data, such as requests from a simulator or real-world, can interact with the algorithm in
complex ways without knowledge of how or where the algorithm is hosted (platform
independence).
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Figure 57 Integration layout
The following API end points were created for exchange of critical simulator activity:
http://localhost/spda/request/parking
Notify SPDA about a vehicle requesting parking by providing a vehicle ID.
http://localhost/spda/events/parking/occupancy
Notify SPDA about change in parking occupancy by providing the previous
and new occupancy. This is triggered when a vehicle arrives or departs a
parking lot.
http://localhost/spda/events/vehicle/eta
Notify SPDA about updated expected time of arrival by providing a vehicle
ID and an updated expected time of arrival (ETA) as UNIX time. This is
triggered when a vehicle's expected time of arrival is impacted by changed
traffic conditions.
http://localhost/spda/events/vehicle/quit
Notify SPDA about a vehicle that has quit using the parking guidance app by
providing its vehicle ID. This is triggered when a driver shuts down the
parking app or abandons the trip.
The SQL based database was used to store vehicle and parking information, historic
parking trends and parking tariffs (Figure 58).
DatabaseSPDAReal-world/Simulator
RESTful API
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Figure 58 Database layout
Multiple simulations were conducted to evaluate the performance SPDA on case-based
scenarios. These cases were generated by altering the percentage of vehicles using
SPDA (0 – 100%), parking demand level (evening, mid-day and peak hours) and the
length of parking stays (low, medium and high flux) as shown in Figure 59.
Figure 59 Parking Simulator graphical user interface
Furthermore, a comparison of SPDA with opportunistically assisted parking search
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(OAPS) and non-assisted parking search (NAPS) was also conducted. The results of
these simulations are presented the following section.
4.2.2 Baseline simulation scenarios
4.2.2.1 Parking availability
1. Low Availability / High Fluctuation (LA/HF)
In this scenario, 50% of the available parking spots remain occupied during the entire
simulation period. The remaining spots become available or occupied with high
frequency. This simulates a situation where parking is scarce, but the occupancy
changes quickly and allows drivers to find open spots during parking search (Figure
60).
Figure 60 Parking availability LF/HF
2. Medium Availability / Low Fluctuation (MA/LF)
In this scenario, 25% of parking spots remain occupied during the entire simulation
period. The remaining spots become available or occupied with low frequency (cars
park for longer times). This simulates a situation where parking is available, but the
Number of available spots
Time
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occupancy trend changes slowly and causes drivers to cruise longer for available
parking spots (Figure 61).
Figure 61 Parking availability MA/LF
3. Medium Availability / High Fluctuation (MA/HF)
In this scenario, 25% of parking spots remain occupied during the entire simulation
period. The remaining spots become available or occupied with high frequency (shorter
parking times). This simulates a situation where parking is available, and the situation
changes quickly allowing causing drivers to find spots with relative ease (Figure 62).
Number of available spots
Time
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Figure 62 Parking Availability MA/HF
4.2.2.2 Parking demand
1. Demand for low parking availability (LD)
In the low availability baseline case, there are on average 65 free parking spots
available. Therefore, the parking demands stacked around this average value to
achieve the desired results (Figure 63).
Number of available spots
Time
Parking dem
and
Time
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Figure 63 Demand for low parking availability
2. Demand for medium availability (HD)
In the medium availability baseline case, there are on average 65 free parking spots
available. Therefore, the parking demands stacked around this average value to
achieve the desired results (Figure 64).
Figure 64 Demand for medium parking availability
4.2.2.3 Traffic volume
1. Low Volume (LV)
In a low volume condition the network is loaded with a baseline traffic that provides
initially free flow conditions throughout the entire network
2. Medium Volume (MV)
Parking dem
and
Time
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In a medium volume condition, the network is initially loaded to a point where 50%
of the inner-city streets are loaded to a point where travel time increases with every
new arrival.
3. High Volume (HV)
In a high-volume condition, the network is initially loaded to a point where 75% of all
streets are loaded to a point where travel time increases with every new arrival.
4.2.3 Simulation Results
4.2.3.1 Scenario: Low Availability, High Fluctuation, High Demand, High Volume
(LA, HF, HD, HV)
In this scenario, vehicles cruising for parking in peak hour conditions will impact traffic
conditions the most. Therefore, the initial testing scenario for SPDA is under high
traffic volume conditions with high demand for parking and a low parking lot
availability. Occupancy fluctuates at a higher magnitude simulating pick up and drop-
offs during peak hours.
In a series of simulations, the penetration rate of vehicles using SPDA was increased
from 0% to 100%. The results of the simulation are shown in Figure 65. Equipped
vehicles reduce their search time by 75% compared to the base scenario without parking
guidance. A moderate introduction of SPDA based guidance (i.e. 20% penetration rate)
reduces the average search time for non-equipped vehicles by 35%, and improves the
overall traffic performance in the area even further.
Drivers using SPDA based guidance are able to secure parking 30% closer to their
desired destination with only a minor variance based on the penetration rate as shown
in Figure 66.
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Figure 65 Search time for parking (LA, HF, HD, HV)
Figure 66 Average distance to desired location (LA, HF, HD, HV)
0% 10% 20% 30% 50% 75% 100%
Min (no guidance) 140 100 80 80 70 65
Max (no guidance) 199 159 139 139 129 124
Avg (no guidance) 170 129 111 107 99 95
Min (SPDA) 20 15 25 15 15 10
Max (SPDA) 48 44 54 44 44 39
Avg (SPDA) 33 31 40 29 29 24
0
50
100
150
200
250
Searchtime
SPDApenetrationrate(%)
Searchtimeforparking(LA,HF,HD,HV)
0
50
100
150
200
250
300
0 10 20 30 50 75 100
Distance(m)
SPDAPenetrationrate(%)
Averagedistancetodesireddestination(LA,HF,HD,HV) Noguidance SPDA
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Introduction of SPDA based guidance also increases the chance of finding a suitable
parking lot for a vehicle before it abandons the trip (Figure 67). With no guidance, 7%
of the vehicles abandon their trips as compared to 5% of SPDA equipped vehicles. The
results show that SDPA equipped vehicles have a significant advantage over
unequipped vehicles in a scenario where parking is scarce, especially at high
penetration rates.
Figure 67 Percentage of abandoned trips (LA, HF, HD, HV)
4.2.3.2 Scenario: High Availability, High Fluctuation, Low Demand, Low Volume
(HA, HF, LD, LV)
This scenario simulates conditions where there is plenty of parking and a low parking
demand therefore no vehicle is forced to abandon its trip. Results show that SPDA
equipped drivers find a parking lot approximately 85% faster than non-guided vehicles
(less than 10 seconds) at all penetration rates (Figure 68). The search time advantage
remains roughly linear for SPDA equipped vehicles and improves by 33% when
penetration rate is varied to 100%. SPDA equipped vehicles also retain an
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0 10 20 30 50 75 100
Abandonedtrips(%
)
SPDApenetrationrate(%)
Percentageofabandonedtrips(LA,HF,HD,HV) Noguidance SPDA
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approximately 66% shorter distance to their final destination as compared to non-
equipped vehicles at all penetration rates (Figure 69).
Figure 68 Search time for parking (HA, HF, LD, LV)
Figure 69 Average distance to desired destination (HA, HF, LD, LV)
0% 10% 20% 30% 50% 75% 100%
Min (no guidance) 60 60 40 40 50 46
Max (no guidance) 119 119 99 99 108 102
Avg (no guidance) 89 91 70 68 79 71
Min (SPDA) 10 5 10 5 5 5
Max (SPDA) 19 14 15 10 14 14
Avg (SPDA) 15 9 10 14 10 9
0
20
40
60
80
100
120
140Searchtime
SPDApenetrationrate(%)
Searchtimeforparking(HA,HF,LD,LV)
0
50
100
150
200
250
300
0 10 20 30 50 75 100
Distance(m)
SPDApenetrationrate(%)
Averagedistancetodesireddestination(HA,HF,LD,LV)
Noguidance SPDA
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4.2.3.3 Scenario: Medium Availability, Low Fluctuation, High Demand, Medium
Volume (MA, LF, HD, MV)
Previous scenarios assumed a high fluctuation in parking events, which provides more
chances of finding a vacant parking spot for drivers. In case of low fluctuation, the
search time of all vehicles is increased and therefore distance to the desired destination
also increases (Figure 70). SPDA equipped vehicles see a reduction in search time by
80%.
Figure 70 Search time for parking (MA, LF, HD, MV)
The positive impact of SPDA equipped vehicles on search time of vehicles not using
any guidance is around 10% (Figure 70) thus the introduction of SPDA did not benefit
0 10 20 30 50 75 100
Min (no guidance) 60 60 40 40 50 45
Max (no guidance) 159 159 139 139 149 144
Avg (no guidance) 110 109 89 88 99 96
Min (SPDA) 10 5 15 10 10 10
Max (SPDA) 29 24 34 29 29 29
Avg (SPDA) 19 16 24 20 19 20
0
20
40
60
80
100
120
140
160
180
Searchtime
SPDApenetrationrate(%)
Searchtimeforparking(MA,LF,HD,MV)
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non-equipped as prominently as seen in the previous scenarios. This trend is also
observed in average distances travelled to final destination as shown in Figure 71.
Figure 71 Average distance to desired destination (MA, LF, HD, MV)
The results in the aforementioned scenarios show evidence for reduction in cruising
time for parking when a centralised guidance logic is used. Results also correspond to
the queuing theory framework developed in section 3.3.1.
The following scenarios are designed to test performance improvement of SPDA by
introducing a parking prediction model and overall system response to uncertainties
and errors in its input.
4.2.3.4 Scenario: Prediction vs. No prediction
Including a prediction model into the system not only requires more computational
power and but increases the overall complexity of the system. This scenario maps the
improvements in search time for SDPA equipped vehicles vs vehicles without using
any parking guidance at various penetration rates ().
0
50
100
150
200
250
300
0 10 20 30 50 75 100
Distance(m)
SPDApenetrationrate(%)
Averagedistancetodesireddestination(MA,LF,HD,MV) Noguidance SPDA
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Improvements vary from 5-25% and the majority of the gain is established for
penetration rates below 30%. This means that a prediction model will help in the initial
phase of the roll-out of such a system, but will fade over time. Considering that a
successful roll-out will significantly change existing parking patterns, this makes sense,
and is worthwhile considering if the data is available.
Figure 72 Search time for parking (Prediction vs. No prediction)
4.2.3.5 Scenario: Unreliability in systems inputs
While the system performs well in laboratory conditions, the question remains if those
results would hold up in a real-world environment. To validate this proposition, the
following errors are introduced in the input variables of the system:
ETA of vehicles at destination
Availability of free parking lots
Predicted availability of free parking lots
Simulations have shown that the effectiveness of SPDA is most sensitive to estimated
time of arrival, followed by the reliability of parking lot information and errors in its
0
5
10
15
20
25
30
10% 20% 30% 50% 75% 100%
Searchtime
SPDApenetrationrate(%)
Searchtimeforparking
NoPrediction Predcition
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prediction. When errors are introduced, there are significant variations in search time
(between 20% and 40%) as shown in Figure 73. However, the distance to the final
destination is less affected by introducing errors in the system as shown in Figure 74.
Figure 73 Average search time (unreliability in system inputs)
Figure 74 Average distance from destination (unreliability in system inputs)
While search time is always worse in simulations with erratic inputs, walking distance
becomes several times less as compared to the controlled simulations.
0
5
10
15
20
25
30
35
10 20 30 50 75 100
Searchtime
SPDApenetrationrate(%)
Averagesearchtime(unreliabilityinsysteminputs)
Control UnreliableETA UnreliableParking Unreliableprediction
0
50
100
150
200
250
10 20 30 50 75 100
Distance(m)
SPDApenetrationrate(%)
Averagedistancefromdestination(unreliabilityinsysteminputs)
Control UnreliableETA UnreliableParking Unreliableprediction
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4.2.3.6 Comparison with OAPS
A comparison of SPDA with OAPS is presented in and with varied parking and traffic
conditions. SDPA is observed to be approximately 60% more effective than OAPS at
various penetration rates. Moreover, a declining trend in search time is observed for
SPDA equipped vehicles as compared to an increasing one for OAPS. Better
performance at higher penetration rates is explained by collaborative nature of SPDA
equipped vehicles as compared OAPS where vehicles act more indecently and
“selfishly”.
Figure 75 Search time comparison with OAPS (LA, HF, HD, HV)
Figure 76 Search time comparison with OAPS (MA, HF, LD, LV)
0
50
100
150
200
0 10 20 30 50 75 100
Searchtime
Guidancepenetrationrate(%)
SearchtimecomparisonwithOAPS(LA,HF,HD,HV)
Noguidance OAPS SPDA
0
50
100
150
200
0 10 20 30 50 75 100
Searchtime
Guidancepenetrationrate(%)
SearchtimecomparisonwithOAPS(MA,HF,LD,LV)
Noguidance OAPS SPDA
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4.2.3.7 Parking lot utilisation comparison
For evaluating parking lot utilisation in a scenario with parking demand equal to supply,
parking lot utilisation is expected to be close to 100% (10 parked cars per lot). For non-
guided vehicles, it takes approximately 21 minutes for all parking lots to fill up (Figure
77). The utilisation keeps varying over time with some parking lots only utilised to
50%.
Figure 77 Parking Utilisation (Control, High Demand)
Running the same scenario with SPDA equipped vehicles results in parking facility
achieving 100% capacity in 7 minutes (Figure 78) which is 66% faster than a similar
scenario with non-guided vehicles. The utilisation for lots remains dense around
maximum occupancy which points to the fact that vehicles spend less time cruising for
parking.
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1 9 17 25 33 41 49 57 65 73 81 89 97 105
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Parkingoccupancy
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Parkingutilisation(Control,HighDemand) Lot1
Lot2
Lot3
Lot4
Lot5
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Lot9
Lot10
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Figure 78 Parking Utilisation (SPDA, High Demand)
A comparative parking scenario of low fluctuation with non-guided (Figure 79) vs.
SPDA equipped (Figure 80) vehicles shows a similar trend where SPDA equipped
vehicles make parking facility arrive near maximum utilisation earlier and remains that
way for the duration of the simulation.
Figure 79 Parking utilisation (Control, Low fluctuation)
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Figure 80 Parking utilisation (SPDA, Low fluctuation)
Overall, the simulations demonstrate that SPDA based guidance improves the
utilisation of parking facilities as a result of less vehicles wasting time looking for
parking. The reduction in cruising time for parking is expected to not only reduce traffic
congestion but also increase revenue for parking facilities as they remain occupied at
higher levels as compared to scenarios where vehicles do not use SPDA.
4.3 Summary
In the first section of this chapter, I have provided a detailed account of the simulations
conducted to measure the performance of SPDA against NAPS and OAPS. The second
section is composed of a case study to predict parking occupancy using real-world
parking data from Melbourne, Australia.
A review of the system architecture for simulations is as follows:
1. To enable communications between SPDA and the simulator, a RESTful API
was developed.
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Parkingutilisation
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2. A shared database between the simulator and SPDA was developed to exchange
network intensive data. Critical information updates were exchanged via the
API.
3. The simulation was configured to run multiple scenarios by varying:
a. Parking availability
b. Parking availability fluctuation
c. Parking demand
d. Traffic volume
In all simulation results, SPDA outperformed NAPS and OAPS when it came to lower
parking search times and search distances. SPDA was tested against errors in system
input to test resistance against unpredictability. Additionally, comparisons with OAPS
and parking lot utilisation were presented to demonstrate effectiveness over SPDA in
various parking and traffic conditions.
A review of parking forecasting case study is as follows:
1. To predict parking, parking data from Melbourne, Australia was used to validate
the forecasting methodology described in chapter 3.
2. After initial analysis using custom built tools, such as PDV, the data was
prepared for forecasting purposes using feature engineering.
3. Forecasting experiments were setup in Azure machine learning studio
comparing Boosted Decision Tree Regression and Neural Network Regression
were compared side by side.
4. In all experiments, Boosted Decision Tree Regression yielded a consistently
higher value of R2 (0.8 and 0.95) and lower MAE (1.1 and 1.25). It was
concluded that Boosted Decision Tree Regression was better suited for use in
predictive analysis of parking as compared to Neural Networks Regression.
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CHAPTER 5. SUMMARY AND CONCLUSIONS
In this chapter, I have presented a summary of the salient features of this thesis:
1. Purpose this study
2. Current parking guidance systems, their efficiency and limitations
3. Selective parking discovery algorithm and simulation findings
4. Parking occupancy forecasting methodology and case study findings
5. Implications of research
6. Limitations of research
7. Recommendations for further research
8. Conclusion
5.1 Purpose of this study
Parking is a vital component of today’s commute. A car begins its journey from a
parking space and ends in a parking space. Most CBD establishments don’t have the
real estate to allow dedicated on-location customer or employee parking and therefore
relies on public parking resources such as on-street parking. On-street parking is an
attractive option because cheaper and more accessible. Due to its popularity, finding
on-parking in a bustling city can be very trying and frustrating experience. A visit to
downtown if often accompanied by questions like:
Is there going to be vacant parking available when I arrive at my destination?
How far am I willing to walk from the parking lot to my destination?
How much am I willing to pay for parking?
Is parking permitted during the time I want to park?
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Whether drivers are able to find the on-street parking relies on a combination of luck,
past experience and trial and error. Which is why drivers often find themselves circling
around their destinations in search of a parking space. Cruising for parking results is
several problems:
1. Congestion
2. Time wastage
3. Carbon emissions
4. Negative economic impact (increased parking prices, high-income drivers)
5. Driver frustration (accidents, illegal parking, trip abandonment)
While navigations systems like Google Maps help drivers get from point an origin to
destination efficiently, they don't assist in parking search. An average vehicle is parked
almost 97% of the time and used only 3%. Therefore, navigation applications without
parking guidance are incomplete. When comes to on-street parking in cities, the
destination (from a human perspective) is not always the same as car’s destination. The
driver parks the vehicle and then walks towards the true destination. Navigation
systems of today don't make this distinction. Ideally, the routing application should not
only provide the most efficient directions to the destination, but also where to park the
vehicle.
My thesis is an attempt to develop an innovative parking guidance technology based on
Selective Parking Discovery Algorithm (SPDA) which can seamlessly integrate into
any in-car navigation system via an API. The algorithm takes in driver’s parking
preferences as input (destination location, maximum walking distance and price).
Based on this input, the algorithm finds the most suitable parking location for the trip
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using real-time parking occupancy information from sensors installed at parking lots.
Additionally, it analyses historic parking occupancy trends to forecast trends and alerts
drivers when to avoid certain areas during peak times in advance.
Since the algorithm already has information about driver’s parking needs, it
automatically picks the optimal parking location instead of letting the driver choose
from a list of all available parking locations thereby reducing time wasted in cruising
for parking.
5.2 The impact of parking problem, policy design and driver
behaviour
Cruising for parking is a consequence attributed to lack of innovation in parking
management and ineffective parking policy design. An increase in car ownership levels
disrupts the balance between parking supply and demand. Furthermore, Information
asymmetry i.e. driver’s lack of knowledge and/or experience in locating vacant parking
supply results in wasteful parking searches. A parking policy that lacks consideration
for driver heterogeneity and socio-economics is prone to counterproductive effects on
not only an individual’s driving experience but also the social surplus. The negative
effects of cruising for parking include:
Congestion
Carbon emissions
Road safety
Loses in parking revenue due to ineffective parking land use
Driver frustration and illegal parking
Increased parking prices negatively impacting economic activity
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Formulating a parking policy that adheres to all driver classes remains a challenge. The
heterogeneity of drivers is a result of behavioural parking preferences due to differences
in incomes and motivation behind the commute. Parking policies perform differently
based on geography and socio-economic environment. A policy cannot simply be
“plugged” because of its success in a different urban environment. Using pricing as the
only tool for controlling parking demand and supply yields negative economic activity.
When prices are increased, drivers change their parking preferences in terms of location
(and walk longer) and therefore still use personal transport.
There is no consensus on whether getting rid of on-street parking or equalizing on-street
parking costs with off-street parking is a solution to the on-street parking problem.
Where there are merits to on-street parking or not is politically charged topic.
Researchers in the parking sector, however, agree that there is a need for innovative
systems to solve the parking problem.
Driver behaviour review sheds light on the choices made by drivers in search of on-
street parking. Drivers are most sensitive to parking price and walking distance. They
begin their search by choosing a parking location closest to their destination and keep
search in an outward spiral fashion. Drivers search patterns differ based on personal
and trip characteristics. Income level, reason for travel (work or leisure), rationality,
gender and age play an important role in driver search. This also applies to the choice
between on and off-street search and walking habits. Past experience and available of
pre-trip information plays an important role in how drivers perform parking search.
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5.3 Current parking guidance systems, their efficiency and
limitations
Parking guidance systems (PGS) can be classified into passive and active systems.
Passive systems include variable message signs (VMS), radio broadcasts or pamphlets.
Active guidance provides more personalized parking information, generally using
personal computing devices as the medium of parking information sharing.
VMS based PGS are limited in their function due to their physical placement and lack
of information control. Their effectiveness, though undeniable, is still not as impressive
as expected. Active PGS is a comparatively newer technology. Consequently, there is
a lack of published literature on their success. Available literature points to noticeable
impacts, such as reduction in cruising, congestion and loss of revenue. Active PGS
perform extremely well during times of low competition. However, the synchronization
effects become apparent and severe in times of higher completion and performs worse
than unassisted parking search.
5.4 Selective parking discovery algorithm and simulation findings
Current parking guidance systems broadcast parking information to all cars requesting
parking. During peak times, this results in something called the synchronization effect.
An example would be a single parking space becoming visible to multiple competing
cars. These cars end up on the street competing for the same parking spot but only the
car arriving there first is able to park and the rest compete for another spot. This can
create traffic bottlenecks and result in a frustrating parking guidance experience. I have
solved this problem by introducing the Selective Parking Discovery Algorithm (SPDA)
which keeps track of all competing cars on the road. If there is one spot left in a parking
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lot, it is only discoverable by only one car. As the number of drivers using parking
guidance system approaches saturation levels, selective dissemination of parking
information can be made free of synchronization effects and reduce unpleasant
surprises. This was achieved by improving upon two primary areas of existing parking
guidance models:
1. Parking information processing based on stated-preference approach
2. Parking occupancy forecasting
To validate the selective discovery hypothesis, a simulation framework was designed
to compare NAPS with OAPS and SPDA. The simulations covered multiples scenarios.
In all simulations, SPDA yielded significantly lower parking search times as compared
to NAPS and OAPS. At greater penetration rates, SPDA also yielded better parking
utilization, therefore potentially generating more revenue.
5.5 Parking occupancy forecasting methodology and case
study findings
In order to provide pre-trip parking occupancy forecasts to drivers, a 6-step
methodology required to conduct quantitative analysis of on-street parking patterns for
forecasting purposes was formulated. These steps are as follows:
1. Defining goals of forecasting parking (e.g. optimising forecasts for predicting
parking occupancy, rate of arrivals/departures, parking duration etc.)
2. Data collection from various sources, such as electronic sensors, surveys and
revenue.
3. Validation of data by filtering out erroneous data and identification of
outliers.
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4. Preliminary analysis to record unusual observations and decomposing data
for trends, cycles and seasons.
5. Selection of attributes and finding correlations between inputs that provide
best fit for predictive modelling.
6. Modelling of predictive algorithms optimised for maximum accuracy and
cost-effective deployment.
To validate the methodology, a case study was conducted to forecast parking occupancy
in Melbourne, Australia. The City Council of Melbourne has installed in-ground
parking bay sensors in most CBD parking bays. These sensors record parking events,
which is when a vehicle arrives and when it departs. To calculate parking occupancy
over irregular intervals of time on a particular street, a sampling algorithm was
developed to create a Parking Data Visualizer for data mining (PDV). PDV’s
comparative reporting feature was used to quickly identify preliminary trends and
seasons in parking occupancy.
To conduct forecasting experiments, a full year of parking occupancy data at half hour
sample frequency was compiled for street segments. Besides occupancy data, weather
data was also included in the dataset. Feature engineering was utilized to add lagged
variables to data set to capture dynamic trends in parking occupancy. To build and train
predictive models, Boosted Decision Tree Regression and Neural Networks Regression
were used and their accuracy compared.
In all experiments, Boosted Decision Tree Regression algorithm outperformed Neural
Networks by achieving a value of R2 between 0.8 and 0.95 and MAE between 1.1 and
1.25. Therefore, it was concluded that Boosted Decision Tree Regression is a more
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accurate and efficient algorithm for predicting parking occupancy as compared to
Neural Networks and suitable for application in parking occupancy prediction.
5.6 Implications of research
Latest technological advancements in computing and sensor technology has paved the
way for transportation engineers towards automation and data transparency. While
significant work is underway in the area of vehicles using renewable energy, innovation
in the parking industry is hardly playing catch-up. Commuting is an integral part of a
city dweller’s life and considering an averaged car is parked 96.5% of the time, city
planners are finalising realising the importance of efficient dissemination of parking.
With car ownership levels on the rise, making on-parking safe and convenient has
become paramount for city planners.
My thesis is expected to introduce new concepts in parking guidance and encourage
deployment of latest technologies to solve the parking problem of today and the future.
Self-driving cars will also benefit from concepts presented in this thesis. Smart cars will
require an autonomous parking guidance component so they can not only drive
themselves but also park on their own.
5.7 Limitations & recommendations for future research
Limitations and recommendations have been noted in two areas of this research:
simulation framework and parking occupancy forecasting. The aim of the simulation is
not to represent the vehicles' journey and parking movements, but to capture the process
of trying to find parking within a maximum walking distance of desired destination.
Therefore, it is recommended for future works to conduct an online-simulation covering
city-specific traffic behaviour. It is also recommended to conduct microscopic
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simulations to evaluate the effects of active guidance on overall changes in driver
behaviour and safety.
The limitations and recommendations have been outlined for parking forecasting case
study as follows:
1. Time span of data set for forecasting
The Melbourne Parking data used for forecasting purposes was available for 1
consecutive year. It is recommended to conduct further research this area
using data spanning over multiple years to capture parking occupancy trends
for once-a-year events (e.g. Christmas).
2. Number of input data attributes
Data attributes such as events, locations or scheduled road works were not
considered for forecasting purposes. Its recommended to include special
events, proximity of special locations (offices, bars, supermarkets, etc.) in the
data set and analyse their impact on forecast accuracy.
3. Parking profiling
Creation of parking profiles based on location to predict parking violations,
occupancy and revenue was not a focus of the forecasting case study. Further
research is required to study their usefulness in parking policy design as they
may prove vital to forecast parking utilization before commencing structural
development.
4. Black box predictive modelling
Black box modelling technique was used to develop and train two widely used
predictive models using brute force optimisation techniques. Further research
is required into comparing other different types of predictive models and using
clear box modelling.
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5.8 Conclusion
My research has provided a comprehensive overview of the parking problem and an
approach to overcome this problem. Learning from the existing limitations of parking
policy and current parking guidance systems, a new type of parking guidance system
design is presented and its effectiveness demonstrated.
The parking problem has a profound effect on the quality of transportation. The
overhead ranges from largescale effects congestion, carbon emissions and loss of
revenue to more personalised effects such as loss of time, waste of fuel, frustration and
safety. Current parking guidance systems have failed to overcome the parking problem
due to inherent flaws in their design, such as lack of personalisation and information
processing.
To address the limitations in active guidance parking guidance system, the concept of
SPDA was introduced. SPDA architecture was developed and validated using a
simulated environment. Vehicles using SPDA outperformed NAPS and OAPS by
dramatically reducing parking search time.
Using predictively analytics, parking occupancy can be forecasted and used as a pre-
trip planning tool to alter driver behaviour. The devised methodology for forecasting
parking demonstrated high accuracy and suitability for implementation.
144
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APPENDIX
A1. Coursework
AIRS unit (IFN001): Completed.
A2. Research Ethics / Statement
This research is not anticipated to involve into human, animals, genetically modified
organisms or biosafety materials. It will be carried out using computer simulation.
A3. Research Data
Parking data used for forecasting study was obtained from the City Of Melbourne
Source: https://data.melbourne.vic.gov.au/Transport-Movement/Parking-Events-
2014/mq3i-cbxd/data
153
A4. Detailed figures
Figure 81 Detailed view of SPDA and interfacing with simulator
154
A5. Results of Additional Forecasting Experiments
STREET SEGMENT BETWEEN STREETS
BOOSTED DECISION TREE REGRESSION
NEURAL NETWORK REGRESSION
Collins Exhibition Spring Columns (by order of importance)
1. occupancy_last_week 2. hour 3. occupancy_yesterday 4. occupancy_last_month 5. occupancy_last_fornight 6. weekday 7. hum 8. wind 9. workingday
1. occupancy_last_week 2. occupancy_last_fornight 3. occupancy_last_month 4. occupancy_yesterday 5. hour 6. weekday 7. workingday 8. temp 9. hum 10. wind 11. day 12. min
MAE 1.495705 R2 0.916921 MAE 1.905139 R2 0.888149
Collins Harbour
Esplanade
Navigation
Drive
Columns (by order of importance)
1. occupancy_yesterday 2. weekday 3. occupancy_last_fornight 4. hour 5. occupancy_last_week 6. workingday 7. occupancy_last_month 8. hum 9. rain
1. occupancy_last_week 2. occupancy_yesterday 3. occupancy_last_month 4. occupancy_last_fornight 5. workingday 6. month 7. day
MAE 1.266842 R2 0.845773 MAE 1.567089 R2 0.814958
Collins King William Columns (by order of importance)
1. occupancy_last_week 2. hour 3. occupancy_last_fornight 4. weekday 5. workingday 6. occupancy_last_month 7. occupancy_yesterday 8. day 9. min 10. hum 11. rain
1. occupancy_last_week 2. occupancy_last_month 3. occupancy_last_fornight 4. occupancy_yesterday 5. hour 6. hum 7. month 8. min 9. rain 10. day
MAE 1.571469 R2 0.894129 MAE 2.153517 R2 0.871431
Flinders Queen Elizabeth Columns (by order of importance)
1. occupancy_yesterday 2. weekday 3. occupancy_last_fornight 4. hour 5. occupancy_last_week 6. workingday 7. occupancy_last_month 8. hum 9. rain
1. occupancy_last_week 8. occupancy_yesterday 9. occupancy_last_month 10. occupancy_last_fornight 11. workingday 12. month 13. day
MAE 1.266842 R2 0.845773 MAE 1.567089 R2 0.814958
155
Flinders Russell Exhibition Columns (by order of importance)
1. occupancy_last_week 2. occupancy_last_fornight 3. hour 4. occupancy_yesterday 5. occupancy_last_month 6. weekday 7. workingday 8. wind 9. temp
1. occupancy_last_week 2. occupancy_last_fornight 3. occupancy_last_month 4. occupancy_yesterday 5. weekday 6. hour 7. hum 8. workingday 9. month 10. day 11. min
MAE 1.520768 R2 0.855416 MAE 1.785318 R2 0.840472
Flinders Spencer King Columns (by order of importance)
1. occupancy_last_week 2. occupancy_last_fornight 3. hour 4. occupancy_last_month 5. occupancy_yesterday 6. weekday 7. workingday 8. wind 9. temp
1. occupancy_last_week 2. occupancy_last_fornight 3. occupancy_last_month 4. occupancy_yesterday 5. weekday 6. workingday 7. hum 8. hour 9. day 10. month 11. min
MAE 2.126095 R2 0.876791 MAE 2.581763 R2 0.854964
King Batman Jeffcott Columns (by order of importance)
1. occupancy_last_week 2. occupancy_last_fornight 3. hour 4. occupancy_last_month 5. occupancy_yesterday 6. weekday 7. workingday 8. wind 9. min 10. day 11. temp 12. hum
1. occupancy_last_week 2. occupancy_yesterday 3. occupancy_last_month 4. occupancy_last_fornight 5. workingday 6. month 7. day
MAE 1.185147 R2 0.912605 MAE 1.740402 R2 0.854697
King Flinders Lane
Flinders Columns (by order of importance)
1. occupancy_last_week 2. occupancy_last_fornight 3. hour 4. occupancy_last_month 5. occupancy_yesterday 6. weekday 7. workingday 8. wind 9. min 10. day 11. temp 12. hum
1. occupancy_last_week 2. occupancy_last_fornight 3. occupancy_last_month 4. hour 5. temp 6. day 7. rain 8. min
MAE 1.520768 R2 0.912605 MAE 2.425776 R2 0.764422
156
King Collins Flinders Lane
Columns (by order of importance)
1. occupancy_last_week 2. occupancy_yesterday 3. occupancy_last_fornight 4. hour 5. weekday 6. occupancy_last_month 7. workingday 8. min 9. day 10. wind
1. occupancy_last_fornight 2. occupancy_last_week 3. occupancy_last_month 4. hour 5. temp 6. min 7. rain
MAE 1.503599 R2 0.922172 MAE 2.08462 R2 0.895056
Bourke Russell Exhibition Columns (by order of importance)
1. occupancy_last_week 2. occupancy_last_fornight 3. occupancy_yesterday 4. hour 5. occupancy_last_month 6. hum 7. workingday 8. weekday 9. temp
1. occupancy_last_week 2. occupancy_last_fornight 3. occupancy_last_month 4. occupancy_yesterday 5. hour 6. hum 7. month 8. min
MAE 2.592342 R2 0.862835 MAE 2.425776 R2 0.764422
Bourke William Queen
Columns (by order of importance)
1. occupancy_last_week 2. occupancy_last_fornight 3. hour 4. weekday 5. occupancy_yesterday 6. occupancy_last_month 7. min 8. workingday 9. rain 10. hum 11. temp
1. occupancy_last_week 2. occupancy_last_fornight 3. hour 4. occupancy_yesterday 5. occupancy_last_month 6. weekday 7. workingday 8. temp 9. hum 10. min 11. month 12. day 13. rain
MAE 1.561726 R2 0.913005 MAE 1.980941 R2 0.899371
