China Safety Science Journal ›› 2019, Vol. 29 ›› Issue (S2): 1-9.doi: 10.16265/j.cnki.issn1003-3033.2019.S2.001

• Safety Systematology •     Next Articles

Progress and perspective of data-driven train delay propagation

WEN Chao1,2, LI Zhongcan1, HUANG Ping1,2, TIAN Rui3, MOU Weiwei1,2, LI Li1,2*   

  1. 1 National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
    2 National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
    3 Dispatching Department, China National Railway Co. Ltd., Beijing 100844, China
  • Received:2019-08-01 Revised:2019-10-08 Online:2019-12-30 Published:2020-10-28

Abstract: The research aims to summarize and analyze the latest progress of train delay propagation and to clarify the application status of data-driven methods. Firstly, the connotation of delay propagation wasexpounded, and the delay propagation process was analyzed.The researches on delay propagation driven by traditional mathematical methods were analyzed. Next, the data-driven models of train delay propagation werereviewed, and the application of statistical models, computational intelligence, and machine learning models wereanalyzed for delay prediction and delay recoverywhich are two key issues fordelay propagation. Finally, the shortcomings of existing researches were concludedfrom four aspects andthe trend of future research was pointed out.The results show thatthe proposed train delay propagation and recovery models based on artificial intelligence and deep learning can assist dispatchers in improving the quality of dispatching decisions and reducing the workload of dispatchers.

Key words: railway, real-world train operation data, delay prediction, delay recovery, delay propagation, data-driven models

CLC Number: