bus real-time arrival prediction using statistical pattern recognition technique

424 Pages·2013·10.91 MB·English

BUS REAL-TIME ARRIVAL PREDICTION USING
STATISTICAL PATTERN RECOGNITION TECHNIQUE
By
Nam Hoai Vu, M.Sc., (2000)
Hanoi University of Civil Engineering, Vietnam
A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
Department of Civil and Environmental Engineering
Carleton University
Ottawa, Ontario, Canada
© December 2006
Nam Hoai Vu
The Doctor of Philosophy in Civil Engineering is a joint program with the University of
Ottawa, administrated by the Ottawa-Carleton Institute for Civil Engineering
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Abstract
Given the realization that real-time bus arrival information is viewed positively by
passengers of public transit, many bus transit agencies of various sizes are developing
Real-time Bus Arrival Information System (RETBAIS) following the implementation of
Automatic Vehicle Location (AVL) and Automatic Passenger Counter (APC) systems.
This research focuses on one important element of the RETBAIS, the real-time prediction
model. Data required for the research were retrieved from the APC and AVL systems of
the City of Ottawa/OC Transpo. The developed model has two main modules: Running
Time Prediction Module (RTM) and Dwell Time Prediction Module (DTM).
The RTM is based on the statistical pattern recognition methodology. Given a
pattern defining bus running time being predicted, the trained RTM automatically
searches through the historical patterns which are the most similar to the new pattern and
based on that, the prediction of a bus running time is made. The RTM was tested with
different data sets of various bus running time situations. It was found that it worked
well as indicated by the average relative prediction error of as low as 5% for the
Transitway route and about 7% for the mixed-traffic bus route. Moreover, this module
performed in a consistent manner even when unusual bus operational scenarios were
used.
The DTM has four sub-modules. The first two sub-modules are also based on a
recognition technique for predicting separately the number of passengers boarding and
alighting. The third sub-module is used to examine the relationship between actual dwell
times and various explanatory variables. The last one is based on the fact that passengers
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choose the most convenient door for boarding and alighting. Having tested with datasets,
the DTM proved that it can predict passenger activities with satisfactory accuracy without
any specific prior assumptions on the complicated relationship between dwell time and
the influencing factors.
When the constituent modules are integrated, the whole model can predict bus
arrival times at every downstream stop. The prediction accuracy increased with new data
availability. The average relative prediction error varied from 3 to 8%.
In order to provide bus dispatchers with tools for managing bus fleet, two
methods to detect bus on-time performance and bus bunching were developed. By using
these tools, a bus dispatcher can easily know ahead of time if the bus is on-time, late,
early, or bunching is likely to occur.
By offering fast, accurate and reliable predictions, it is contended that the
developed real-time prediction model will enhance the bus arrival information system
and therefore will be a contribution to public transportation operation.
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Acknowledgments
I would like to express my deepest gratitude to Prof. Ata. M. Khan for
encouragement, patience, support and invaluable scientific guidance in supervising this
thesis.
I am grateful to the staffs of the OC Transpo (Ottawa, Canada) for several
discussions, comments and data provisions on the various aspects of this thesis. Special
thanks to Mr. Joe Koffman, Mr. Brian Barclay and Ms. Sylvie Paquette. I would like to
say thank you to Mr. Kean Lew and Mrs. Stephen Hotard (PTV America Inc.) for helping
me to use the VISSIM software.
I am greatly thankful to Prof. Yasser Hassan, Prof. William Johnson, and Prof.
Steven Prus for valuable suggestions on this thesis research.
Financial support by the Vietnamese Government is gratefully acknowledged.
I want to thank my friends; Mr. Phung Viet Anh who was always willing to help
me during difficulties; Mr. Jarbar Siddique for interesting conversations in the common
favorite area of bus transit; Ms. Sandra Majkik who shared data with me.
I am so indebted to my wife Mrs. Huyen Vu and to my son Hieu Vu for
continuous encouragements, patience, sacrifice and their love. I love you both.
Four years of living and studying in this country tattooed in my mind about a
beautiful country with clement people. Thank you Canada!
Nam Hoai Vu
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TABLE OF CONTENTS
ABSTRACT.............................................................................................................................i
ACKNOWLEDGMENTS...................................................................................................iii
TABLE OF CONTENTS.....................................................................................................iv
LIST OF TABLES...............................................................................................................xi
LIST OF FIGURES............................................................................................................xv
ABBREVIATIONS..........................................................................................................xviii
CHAPTER 1: INTRODUCTION
1.1 Overview.......................................................................................................................1
1.2 Background...................................................................................................................4
1.3 Problem Statement.......................................................................................................6
1.4 Goals and Objectives....................................................................................................7
1.5 Study Methodology......................................................................................................8
1.6 Thesis Document and Organization.........................................................................11
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction................................................................................................................14
2.2 Real-Time Bus Arrival Information System: Current State of Development... 14
2.2.1 AVL System and APC System.....................................................................15
2.2.1.1 Automatic Vehicle Location System........................................15
2.2.1.2 Automatic Passenger Counting System....................................19
2.2.1.3 Uses of Retrieved AVL-APC data in RETBAIS......................21
2.2.2 Bus Running Time Prediction Algorithms.................................................25
2.2.2.1 Blacksburg (Virginia) Prediction Algorithms............................25
22.2.2 Portland (Oregon) and King County Metro, Seattle
(Washington) Prediction Algorithm..........................................26
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2.2.2.3 Texas Transportation Institute Algorithms...............................28
2.2.2.4 New Jersey University Algorithm............................................28
2.2.2.5 University of Toronto Algorithm...............................................29
2.2.2.6 Artificial Neural Networks (ANN)-Based Algorithms...........30
2.2.2.7 Classical Statistical Regression Models....................................31
2.2.2.8 Other Prediction Models............................................................33
2.2.3 Bus Dwell Time Prediction Models............................................................35
2.2.4 Media and Communication System............................................................38
2.2.4.1 Information Dissemination Media.............................................39
2.2.4.2 Communication System..............................................................40
2.3 Summary....................................................................................................................40
CHAPTER 3: MECHANISM OF THE PROPOSED MODEL
AND AVL -APC DATA COLLECTION
3.1 Introduction..................................................................................................................42
3.2 Component of Bus Trip Time and Influencing Factors.............................................42
3.2.1 Actual Moving Time....................................................................................44
3.2.2 Dwell Time...................................................................................................45
3.2.3 Traffic Signal Delay....................................................................................46
3.2.4 General Delay...............................................................................................46
3.2.5 Recovery Time..............................................................................................48
3.3 Proposed Structures and Components........................................................................48
3.3.1 General Discussion......................................................................................48
3.3.2 Assumptions..................................................................................................50
3.3.3 Structure and the Building Modules of the Proposed Model.....................51
3.3.4 Mechanism of the Proposed Model............................................................53
3.4 APC and AVL Data Collection...................................................................................59
3.4.1 Bus Route Selection.....................................................................................60
3.4.2 Data Sets.......................................................................................................62
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3.4.3 APC and AVL Data Description.................................................................64
3.4.4 Data Characteristics....................................................................................66
3.4.4.1 Arrival Time....................................................................................66
3.4.4.2 Dwell Time and Passenger Activities............................................76
3.5 Summary.....................................................................................................................82
CHAPTER 4: BUS RUNNING TIME PREDICTION MODULE
4.1 Introduction.................................................................................................................83
4.2 Problem Statement.....................................................................................................84
4.3 Nonparametric Regression Methods in Statistical Pattern Recognition.................85
4.3.1 LOWESS Estimation Method....................................................................87
4.3.1.1 General Functions.........................................................................87
4.3.1.2 Bandwidth Selection.....................................................................89
4.3.1.3 Weighting Kernel Function Selection.........................................93
4.3.14 Degree of Polynomial Regression...............................................93
4.4 Modelling Bus Running Time Prediction by using LOWESS Method and
APC -AVL Data..........................................................................................................94
4.4.1 APC and AVL data......................................................................................94
4.4.2 Parameter Selection.....................................................................................94
4.4.3 Pattern Selection..........................................................................................95
4.4.4 Pattern Recognition.....................................................................................96
4.4.4.1 Euclidean Distance Calculation...................................................96
4.4.4.2 Optimal Bandwidth by Leave-one-out Method.........................96
4.4.4.3 Recognition of the Neighbours...................................................100
4.4.5 Prediction....................................................................................................100
4.4.6 Update Prediction......................................................................................102
4.5 Summary.....................................................................................................................103
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CHAPTER 5: BUS DWELL TIME PREDICTION MODULE
5.1 Introduction...............................................................................................................104
5.2 Dwell Time Prediction Module.................................................................................105
5.2.1 Discussions on Previous Works.................................................................105
5.2.2 Dwell Time Prediction Module (DTM).....................................................108
5.2.3 Real-Time Boarding Passenger Prediction Sub-module...........................110
5.2.3.1 Parameter Selection.....................................................................110
5.2.3.2 Pattern Selection..........................................................................110
5.2.3.3 Pattern Recognition......................................................................114
5.2.3.4 Prediction.....................................................................................114
5.2.3.5 Update Prediction........................................................................115
5.2.4 Real-time Alighting Passenger Prediction Sub-module...........................115
5.2.4.1 Parameter Selections....................................................................115
5.2.4.2 Pattern Selection..........................................................................116
5.2.4.3 Pattern Recognition......................................................................116
5.2.4.4 Prediction......................................................................................117
5.2.4.5 Update Prediction.........................................................................117
5.2.5 Regression Sub-Module.............................................................................118
5.2.5.1 Variables Selection and Preparation...........................................118
5.2.5.2 Regression Functions...................................................................120
5.2.6 Busiest Door Prediction Sub-Module........................................................127
5.2.6.1 Rigid-Body Bus...........................................................................128
5.2.6.2 Articulated Bus............................................................................136
5.2.7 Method A vs. Method B and the Selection................................................142
5.2.7.1 Method A......................................................................................143
5.2.1.2 Method B......................................................................................143
5.2.13 Rigid-body Bus.............................................................................144
5.2.7.4 Articulated Bus.............................................................................144
5.2.7.5 Accuracy Performance of the Two Methods..............................146
5.3 Summary.....................................................................................................................147
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CHAPTER 6: MODEL PERFORMANCE
6.1 Introduction......................................................................................149
6.2 Computer Program Development....................................................................150
6.3 Simulation of Bus Operation Scenarios................................................................150
6.3.1 The VISSIM Simulator...............................................................................151
6.3.2 Transit Route Coding in VISSIM..............................................................153
6.3.3 Calibration and Validation..........................................................................155
6.3.4 Bus Operation Scenarios, Micro-simulation Runs,
and the VISSIM Outputs............................................................................157
6.4 Model Testing and Comparison............................ 162
6.4.1 Evaluation Criteria for Prediction Performance........................................162
6.4.2 Reference Predictors...................................................................................163
6.4.2.1 The Naive Model..........................................................................164
6.4.2.2 The Kalman Filter- Based Model..............................................164
6.4.3 Testing the Developed Model and the Reference Predictors....................166
6.4.3.1 Data Issues..................................................................................166
6.4.3.2 Running Time Prediction Performance....................................167
6.4.3.3 Boarding Passenger Prediction Performance...........................176
6.4.3.4 Alighting Passenger Prediction Performance...........................178
6.4.3.5 Prediction Performance with Actual Data................................180
6.4.3.6 Tukey Test for Performance Comparison.................................184
6.4.3.7 Bus Arrival Time Prediction Performance...............................186
6.5..............................Summary........................................................................................188
CHAPTER 7: REAL-TIME PREDICTION INTERVAL,
ON-LINE SCHEDULE ADHERENCE EVALUATION AND
BUS BUNCHING DETECTION
7.1 Introduction......................................................................................190
7.2 Real-time Prediction Interval........................................................................190
7.3 On-line Schedule Adherence Evaluation...............................................................194
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