Prediction of different types of train delay of Guangzhou-Shenzhen high-speed railway

doi:10.16265/j.cnki.issn1003-3033.2019.S2.030

Abstract

Abstract: In order to ensure that high-speed railway trains operate according to schedule and reduce the risk of potential traffic conflicts, the delay of high-speed trains and their propagation rules were studied by using the train operation performance. Firstly, through the descriptive statistical analysis of the actual train operation data of the Guangzhou-Shenzhen high-speed railway, the primary delay distribution of the train operation was obtained. Secondly, the hierarchical clustering algorithm was used to analyze the delayed trains, and four kinds of delayed train sequences were obtained. Based on this, the delay feature variables were extracted. Finally, the random forest model was used to predict delay time of all kinds of delay train sequences. The results show that combined with the random forest model, the accuracy of predicting delay time of four kinds of delayed trains is more than 84%.

Key words: high-speed railway, train delays, data driven, hierarchical clustering, random forest model

CLC Number:

X951

HU Rui, XU Chuanling, FENG Yongtai, WEN Chao, WANG Quanquan. Prediction of different types of train delay of Guangzhou-Shenzhen high-speed railway[J]. China Safety Science Journal, 2019, 29(S2): 181-186.

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