Delay recovery model for high-speed trains with compressed train dwell time and running time

作     者：Yafei Hou Chao Wen Ping Huang Liping Fu Chaozhe Jiang Yafei Hou;Chao Wen;Ping Huang;Liping Fu;Chaozhe Jiang

作者机构：National United Engineering Laboratory of Integrated and Intelligent TransportationSouthwest Jiaotong UniversityChengdu 610031China National Engineering Laboratory of Integrated Transportation Big Data Application TechnologySouthwest Jiaotong UniversityChengdu 610031China Intelligent Transport Systems CenterWuhan University of TechnologyWuhan 430070China 

出 版 物：《Railway Engineering Science》 (铁道工程科学（英文版）)

年 卷 期：2020年第28卷第4期

页      面：424-434页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 082303[工学-交通运输规划与管理] 0802[工学-机械工程] 0814[工学-土木工程] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 0823[工学-交通运输工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Nature Science Foundation of China(Nos.71871188 and U1834209) the Science and Technology Department of Sichuan Province(No.2018JY0567) 

主　　题：High-speed train Delay recovery Train operation adjustment actions Gradient-boosted regression tree 

摘      要：Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of *** 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 ***,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 *** 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 ***,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.