数据驱动的列车晚点传播研究*

doi:10.16265/j.cnki.issn1003-3033.2019.S2.001

中国安全科学学报 ›› 2019, Vol. 29 ›› Issue (S2): 1-9.doi: 10.16265/j.cnki.issn1003-3033.2019.S2.001

• 安全系统学 • 下一篇

数据驱动的列车晚点传播研究^*

文超^1,2 副教授, 李忠灿^1,2, 黄平^1,2, 田锐³, 牟玮玮^1,2, 李力^**1,2 讲师

1 西南交通大学综合交通运输国家地方联合工程实验室,四川成都 610031;
2 西南交通大学综合交通大数据应用技术国家工程实验室,四川成都 610031;
3 中国国家铁路集团有限公司调度部,北京 100844

收稿日期:2019-08-01 修回日期:2019-10-08 出版日期:2019-12-30 发布日期:2020-10-28
通讯作者: ** 李力(1985—),女,湖北恩施人,博士,讲师,主要从事运输组织优化方面的研究。E-mail:speciallili@ swjtu.edu.cn。
作者简介:文超 (1984—),男,江西宜春人,博士,副教授,博士生导师,主要从事铁路运输组织优化、交通大数据应用等方面的研究。Email: wenchao@swjtu.cn。
基金资助:
国家自然科学基金资助(71871188, U1834209);四川省科技厅应用基础研究项目(2018JY0567)。

Progress and perspective of data-driven train delay propagation

WEN Chao^1,2, LI Zhongcan¹, HUANG Ping^1,2, TIAN Rui³, MOU Weiwei^1,2, LI Li^1,2*

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

摘要/Abstract

摘要： 为分析和总结铁路列车晚点传播问题的最新进展,深入研究数据驱动方法的应用情况,首先,阐述晚点传播问题内涵,了解晚点传播过程;其次,简要分析基于传统数学模型驱动的晚点传播研究情况;然后,重点综述列车晚点传播的数据驱动模型,针对晚点预测和晚点恢复2个关键问题,分析统计模型、智能计算和机器学习3类数据驱动方法的应用情况;最后,总结已有研究存在的4方面不足,指出未来研究的趋势。结果表明:基于人工智能、深度学习构建列车晚点传播及恢复模型可以辅助调度员提高调度决策质量,降低调度员工作负荷。

关键词: 铁路, 列车运行实绩, 晚点预测, 晚点恢复, 晚点传播, 数据驱动模型

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

中图分类号:

X951

文超, 李忠灿, 黄平, 田锐, 牟玮玮, 李力. 数据驱动的列车晚点传播研究^*[J]. 中国安全科学学报, 2019, 29(S2): 1-9.

WEN Chao, LI Zhongcan, HUANG Ping, TIAN Rui, MOU Weiwei, LI Li. Progress and perspective of data-driven train delay propagation[J]. China Safety Science Journal, 2019, 29(S2): 1-9.

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数据驱动的列车晚点传播研究^*

Progress and perspective of data-driven train delay propagation

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