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 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 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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 i Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 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. ii Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 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 m Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 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 iv Reproduced with permission