Delay recovery model for high-speed trains with compressed train dwell time and running time
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Delay recovery model for high-speed trains with compressed train dwell time and running time Yafei Hou1,2 • Chao Wen1,2 • Ping Huang1,2 • Liping Fu3 • Chaozhe Jiang1,2
Received: 9 May 2020 / Revised: 28 October 2020 / Accepted: 3 November 2020 / Published online: 24 November 2020 Ó The Author(s) 2020
Abstract Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers. In this study, the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables. First, the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions, namely the compression of the train dwell time at stations and the compression of the train running time in sections. Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time, namely the delay time, the scheduled supplement time, the running interval, the occurrence time, and the place where the delay occurred, under the two train operation adjustment actions. Finally, the gradient-boosted regression tree (GBRT) algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions. A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.
& Chao Wen [email protected] 1
National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, China
2
National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 610031, China
3
Intelligent Transport Systems Center, Wuhan University of Technology, Wuhan 430070, China
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Keywords High-speed train Delay recovery Train operation adjustment actions Gradient-boosted regression tree
1 Introduction High-speed trains encounter many random disturbances during operation that can cause train delays. The anti-interference ability of the train timetable and the ability to recover from delays after being affected by disturbances are among the most critical concerns that affect the service quality of high-speed railways. Delay recovery refers to the reduction of the delay time, and effective train delay recovery is a top priority in the daily work of dispatchers. When a train is delayed, the dispatcher should select an appropriate train operation adjustment action to coordinate with the operation situation and transport needs; this decision is made on the basis of the dispatcher’s own experience and, of course, the dispatching rules. There are three widely used train adjustment actions, namely compressing the train dwell time at stations, compressing the train running time in sections, and changing the travel inte
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