Development of a maximum likelihood regression tree-based model for predicting subway incident delay |
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| Institution: | 1. College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China;2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;3. Griffith School of Engineering, Griffith University, Gold Coast, 4222 QLD, Australia;1. State Key Laboratory for Geomechanics & Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China;3. Institute of Engineering Management, Hohai University, Nanjing 211100, China;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China;3. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, China;1. Northeastern University, Boston, MA 02115, USA;2. Massachusetts Institute of Technology, Cambridge, MA 02139, USA;1. Sol Price School of Public Policy, University of Southern California, United States;2. Center for Risk and Economic Analysis of Threats and Emergencies, University of Southern California, United States;3. Sonny Astani Department of Civil and Environmental Engineering, University of Southern California, United States;4. City and Regional Planning, Knowlton School of Architecture, The Ohio State University, United States;1. School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China;2. School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China |
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| Abstract: | This study aims to develop a maximum likelihood regression tree-based model to predict subway incident delays, which are major negative impacts caused by subway incidents from the commuter’s perspective. Using the Hong Kong subway incident data from 2005 and 2009, a tree comprising 10 terminal nodes is selected to predict subway incident delays in a case study. An accelerated failure time (AFT) analysis is conducted separately for each terminal node. The goodness-of-fit results show that our developed model outperforms the traditional AFT models with fixed and random effects because it can overcome the heterogeneity problem and over-fitting effects. The developed model is beneficial for subway engineers looking to propose effective strategies for reducing subway incident delays, especially in super-large-sized cities with huge public travel demand. |
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| Keywords: | Subway incidents Delay Maximum likelihood regression tree Accelerated failure time |
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