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Travel Time Prediction Model for Regional Bus Transit

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Travel Time Prediction Model for Regional Bus Transit TRAVEL TIME PREDICTION MODEL FOR REGIONAL BUS TRANSIT by Andrew Chun Kit Wong A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Department of Civil Engineering University of Toronto © Copyright by Andrew Chun Kit Wong 2009 Travel Time Prediction Model for Regional Bus Transit Andrew Chun Kit Wong Master of Applied Science Department of Civil Engineering University of Toronto 2009 Abstract Over the past decade, the popularity of regional bus services has grown in large North American cities owing to more people living in suburban areas and commuting to the Central Business District to work every day. Estimating journey time for regional buses is challenging because of the low frequencies and long commuting distances that typically characterize such services. This research project developed a mathematical model to estimate regional bus travel time using artificial neural networks (ANN). ANN outperformed other forecasting methods, namely historical average and linear regression, by an average of 35 and 26 seconds respectively. The ANN results showed, however, overestimation by 40% to 60%, which can lead to travellers missing the bus. An operational strategy is integrated into the model to minimize stakeholders’ costs when the model’s forecast time is later than the scheduled bus departure time. This operational strategy should be varied as the commuting distance decreases. ii Acknowledgments I would like to express my sincere gratitude to Dr. Amer Shalaby and Dr. Baher Abdulhai for their supervision during the studies of my master of applied science degree. Their guidance and constant inspiration throughout my graduate studies are very much appreciated. I would also like to give many thanks to my professors in the Transportation Group, fellow graduate students and administration staff at the ITS Centre and Testbed, including but not limited to Asmus Georgi, Bilal Farooq, Bryce Sharman, Dr. Eric Miller, Dr. Matthew Roorda, Farhad Shahla, Hossam Abd El-Gawad, Karen Woo, Marcus Williams, Mahmoud Osman, Michael Hain, Rinaldo Cavalcante, Wen Xie, Wenli Gao, Yang Hao Jiang, Yasmin Shalaby, and more, for their help and support during my research. Thanks to the Greater Toronto Transit Authority – GO Transit and the Ministry of Transportation of Ontario for providing variable GO buses’ GPS data and loop detectors data for this research project. Special thanks to Cally Cheung for her assistance in proofreading my thesis. Last but not least, I would like to express my appreciation to my family, Cally Cheung, and my dear friends for their continuous encouragement throughout my graduate studies. iii Table of Contents Acknowledgments.......................................................................................................................... iii Table of Contents........................................................................................................................... iv List of Tables ............................................................................................................................... viii List of Figures............................................................................................................................... xii List of Appendices ....................................................................................................................... xiv Chapter 1 Introduction .................................................................................................................... 1 1 Introduction................................................................................................................................ 1 1.1 Research Background.........................................................................................................1 1.2 Thesis Objectives................................................................................................................ 4 1.3 Thesis Scope....................................................................................................................... 5 1.4 Thesis Organization............................................................................................................ 5 Chapter 2 Literature Review........................................................................................................... 7 2 Literature Review....................................................................................................................... 7 2.1 Univariate Models............................................................................................................... 7 2.2 Multivariate Models............................................................................................................ 8 2.2.1 Regression Models.................................................................................................. 8 2.2.2 Kalman Filtering Models ...................................................................................... 10 2.3 Artificial Neural Networks ............................................................................................... 12 2.4 Other Forecasting Models................................................................................................. 14 Chapter 3 Data .............................................................................................................................. 17 3 Data .......................................................................................................................................... 17 3.1 Data Collection................................................................................................................. 17 3.1.1 Bus Schedules....................................................................................................... 17 iv 3.1.2 Global Positioning System (GPS) Data of Bus Locations.................................... 17 3.1.3 Loop Detector Data............................................................................................... 18 3.1.4 Incident Reports.................................................................................................... 21 3.1.5 Historical Daily Weather Conditions.................................................................... 21 Chapter 4 Travel Time Computation and Descriptive Analysis................................................... 24 4 Travel Time Computation and Descriptive Analysis............................................................... 24 4.1 Regional Bus Journey Time Computation........................................................................ 24 4.1.1 Checkpoint Identification...................................................................................... 24 4.1.2 Procedure of Computing Bus Travel Time........................................................... 27 4.2 Regional Bus Journey Time Performance Analysis ......................................................... 31 4.2.1 Gardiner Expressway Eastbound Route................................................................ 33 4.2.2 Gardiner Expressway Westbound Route .............................................................. 34 4.2.3 Lakeshore Boulevard Eastbound Route................................................................ 35 4.2.4 Lakeshore Boulevard Westbound Route .............................................................. 35 4.3 Limitations ........................................................................................................................ 36 Chapter 5 Artificial Neural Network ............................................................................................ 41 5 Artificial Neural Network ........................................................................................................ 41 5.1 Theoretical Background.................................................................................................... 41 5.1.1 Basic Unit of Artificial Neural Network: Neuron................................................. 41 5.1.2 Selection of Artificial Neural Network Model ..................................................... 42 5.1.3 Advantages and Disadvantages of Artificial Neural Network.............................. 42 5.1.4 Artificial Neural Network’s Transfer Function .................................................... 45 5.1.5 Feedforward Neural Network vs. Feedbackward Neural Network ...................... 46 5.1.6 Artificial Neural Network Training Techniques................................................... 46 5.1.6.1 Supervised Learning Techniques........................................................... 46 5.1.6.2 Unsupervised Learning Techniques....................................................... 47 v 5.1.7 Multilayer Feedforward Perceptron with Backpropagation ................................. 47 5.1.8 Input Component Simplification Techniques ....................................................... 51 5.1.8.1 Sensitivity Analysis................................................................................ 51 5.1.8.2 Principal Component Analysis ............................................................... 53 5.1.9 Over Fit Training Data Avoidance ....................................................................... 53 5.1.10 Performance Measures.......................................................................................... 54 5.2 Artificial Neural Network Calibrations ............................................................................ 54 5.2.1 Sensitivity Analysis.............................................................................................
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