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Title Bus Bunching Prediction and Transit Route Demand Estimation Bus Bunching Prediction and Transit Route Demand Title Estimation Using Automatic Vehicle Location Data( Dissertation_全文 ) Author(s) Sun, Wenzhe Citation 京都大学 Issue Date 2020-05-25 URL https://doi.org/10.14989/doctor.k22653 Right Type Thesis or Dissertation Textversion ETD Kyoto University Bus Bunching Prediction and Transit Route Demand Estimation Using Automatic Vehicle Location Data SUN WENZHE Abstract The availability of massive passive transit data has provided a variety of feasibilities for the public transit operators to deliver better service that is of high reliability and characterizes users’ demand. Among these data sources, Automatic Vehicle Location (AVL), Automatic Fare Collection (AFC) and Automatic Passenger Counting (APC) data are commonly used, and they are playing significant roles in different aspects of improving transit services. AVL data contain the spatiotemporal coordinates of the transit vehicles which not only offer service reliability metrics, but also indicate the general traffic condition. AFC and APC data are presenting passenger demand patterns, including boarding flows, alighting flows, passenger loads and, in case of AFC only, origin-destination (OD) flows. AVL data are easier to obtain compared to AFC data. Existing literature shows the effectiveness of AVL data in predicting future arrival times and headways, and providing useful inputs for the models to control bus vehicles in terms of operation velocity and headway in real time. However, the methodology for using bus AVL data to estimate demand has been fairly undeveloped. This research has two main objectives. Firstly, it aims to illustrate the characteristics of bus AVL data, overview the conventional applications which are using this data source to make predictions on various items of operators’ concern and further extend the existing literature by taking bus bunching as the prediction object. Secondly, it proposes an innovative idea and a novel methodology to estimate bus route OD flows using the bus AVL data as the main data source. It expands the potential of bus AVL data in inferring passenger demand beyond the design purpose of AVL system. This can help the operators having limited access to AFC and APC data. In the first part of the dissertation, a logistic regression model is developed to compute the probability that a bus is going to be caught in bunching instead of answering the question in a deterministic yes- or-no form based on the exact value predicted for headway. The prediction performance of the proposed method is compared with two headway-based methods using linear regression and support vector machine. Bus AVL data from Kyoto City, Japan, are used for the case study. The advantages of the probabilistic method are illustrated by Receiver Operator Characteristic (ROC) analysis in which the ROC curve is interpreted as an efficient front providing the operators with trade-off options. ii In the second part of the dissertation, the feasibility of estimating passenger OD flows for a bus transit route using AVL data is explored. Dwell time models are used to build connections between the information extracted from bus AVL data and passenger OD flows. A modified gravity model is applied to reduce the number of unknown parameters. Bayesian inference and Markov Chain Monte Carlo (MCMC) methods are implemented to estimate the mean and confidence intervals for the unknown parameters. The methodology is validated with bus AVL data from Shizuoka City, Japan where AFC data for nearly all passengers in the same observational period are available and thus can be taken as the ground truth. It is found in the case study that the estimation performance derived by using bus AVL data matches that using independent passenger boarding/alighting counts. Furthermore, additional input data that roughly characterize the importance of bus stops improve the performance of OD estimation. Finally, complex dwell time models considering the effect of various methods of payment, in-vehicle crowding and bus bunching are discussed. It is shown that the estimated demand might be biased in case a too simplistic dwell time model is applied and bus bunching and crowding are frequently occurring. Keywords: Automatic Vehicle Location data; Bus bunching prediction; Origin-destination matrix; Bayesian inference; Bus dwell time iii Acknowledgements At the end of the long hard journey after pursuing the master’s degree and PhD in Japan for more than five years, I am truly grateful to the people who I am luckily connected with for offering me the kind help in any form. First of all, I would like to thank my supervisor Dr. Jan-Dirk Schmöcker, who has been playing a variety of roles and guiding me in these years, including supervisor, mentor, co-author, boss and academia icon as well. You make me feel that pursuing PhD in this lab is one of the best choices I have made in my life. It did not occur to me that I would go this far with respect to research when I started my study in Kyoto University as a master’s course student. And enlightened by the interactive mentorship you provide me, I am now confident that I can go even further. You teach me how to determine the research direction, to find interesting and meaningful topics, to write the research paper, to guide the students, and most importantly the right attitude towards research. With these gifts I can try to build up an academic career, and feel motivated to pass them to my future students. I also thank Prof. Yamada and Prof. Fujii in Kyoto University, who are the committee member of my PhD dissertation and give me insightful comments in the seminars and discussions. And thanks to the current faculty of Intelligent Transportation Systems Laboratory, Prof. Yamada and Dr. Schmöcker again, Dr. Nakao and lab secretary, Ms. Nishikawa, for giving me a lot of help in research and other aspects. Sincere thanks to previous lab faculty, Prof. Uno in Kyoto University, Dr. Nakamura currently in Nagoya University and Dr. Yamazaki who currently goes to the industry. I probably even failed to complete the master’s course if without your help in the initial stage when I started this journey. Also thanks to the previous lab secretaries, Ms. Shii, Ms. Nishimura and Ms. Nagata for melting my inside uncertainties with your warm hearts, helping me in landing the lives in Japan, administrative procedures and so forth. Although you left our lab for various reasons, I will always keep this in my mind. Thanks to the past and present students in our lab. The night for the first time I arrived at Kansai Airport, Japan is like yesterday. Due to technical problems, it took me almost two hours to complete iv the immigration procedure. I was touched when seeing Fujii-kun and Senda-kun waiting for two hours and holding a paperboard with my name. I still keep the paperboard in my drawer, which I think also the souvenir of all the lovely lab members I meet in this journey, classmates, seniors, juniors, colleagues and friends. Apology for my not naming you one by one here. Genuine thanks to Lotte foundation for the financial support in recent two years. This scholarship is indeed timely help to me. Also thank you for the networking events that provide me the opportunity to connect with so many excellent international students in Japan, and the lifelong chocolates. I acknowledge the financial support from the Kyoto Innovative Transportation Research Organization for part of my research in 2018 and 2019. Thanks to Hitachi Laboratory (Hitach R&D Group) in Kyoto University for the financial support and notably Dr. Fukuda for the valuable advices on my research which greatly inspire me. Finally, I would like to thank my parents, Yunfang Sun and Huiying Xu. I feel speechless regarding the words I should send to you after being absent from your side in so many days and nights. The accomplishment of completing this dissertation undoubtedly also belongs to you. v Preface Parts of this dissertation have been published in journals, presented in conferences or else submitted for review are as follows Journal papers I. Sun, W., Schmöcker, J.-D., & Nakamura, T. (2020). On the trade-off between sensitivity and specificity in bus bunching prediction. Journal of Intelligent Transportation Systems (available online). (Chapter 3) Conference presentations I. Sun, W., & Schmöcker, J.-D. (2019). A method to classify bus bunching events using AVL data. Transportation Research Board 98th Annual Meeting, Washington DC, USA. (Chapter 3) II. Sun, W., & Schmöcker, J.-D. (2019). Using AVL data to predict public transport bunching. 60th Autumn Conference of Committee of Infrastructure Planning and Management, Japan Society of Civil Engineers. Toyama, Japan (Chapter 3) III. Sun, W., Schmöcker, J.-D., Nakamura, T., & Shimamoto, H. (2018). Bus bunching prediction based on logistic regression considering rare event bias. CASPT 2018 and TransitData 2018, Brisbane, Australia. (Chapter 3) IV. Sun, W., Schmöcker, J.-D., & Fukuda, K. (2019). Real-time estimation of bus passenger OD patterns based on AVL data. TransitData 2019, Paris, France. (Chapter 4) Under review/ To be submitted II. Sun, W., Schmöcker, J.-D., & Fukuda, K. (2020). Estimation of transit route origin-destination flows using bus AVL data. Transportation research part C: Emerging Technologies (under review). (Chapter 4) III. Sun, W., Schmöcker, J.-D., Fukuda, K., & Nakamura, T. (2020). Estimation of passenger flows through AVL data with different dwell time functions. TransitData 2020, Toronto, Canada (under
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