高速铁路故障时空分布及持续时长分布特征研究*

doi:10.16265/j.cnki.issn1003-3033.2018.S2.018

中国安全科学学报 ›› 2018, Vol. 28 ›› Issue (S2): 99-104.doi: 10.16265/j.cnki.issn1003-3033.2018.S2.018

高速铁路故障时空分布及持续时长分布特征研究^*

黄平^1,2,3, 李忠灿^1,2, 文超^**1,2,3 副教授, 彭其渊^1,2 教授

1 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;
2 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;
3 滑铁卢大学铁路研究中心,加拿大滑铁卢 N2L3G1

收稿日期:2018-10-08 修回日期:2018-11-26 出版日期:2018-12-30 发布日期:2020-11-11
通讯作者: **文超(1984—),男,江西宜春人,博士,副教授,硕士生导师,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。E-mail:wenchao@swjtu.cn。
作者简介:黄平 (1990—),男,四川宜宾人,博士研究生,研究方向为铁路运输组织优化、交通数据分析建模、机器学习等。E-mail:huangping129@my.swjtu.edu.cn。
基金资助:
国家自然科学基金资助(71871188); 国家重点研发计划(2017YFB1200701); 西南交通大学博士研究生创新基金资助(D-CX201827)。

Studyonspatial-temporal and duration distribution characteristics of high-speed railway disruptions

HUANG Ping^1,2,3, LI Zhongcan^1,2, WEN Chao^1,2,3, PENG Qiyuan^1,2

1 National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
2 National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan 610031, China;
3 Railway Research Centre, University of Waterloo, Waterloo N2L3G1, Canada

Received:2018-10-08 Revised:2018-11-26 Online:2018-12-30 Published:2020-11-11

摘要/Abstract

摘要： 研究故障的频率特点及持续时长对实现高速铁路 (HSR)故障的预测以及提高实时调度指挥水平具有重要作用。基于我国高速铁路2014—2015年故障发生实际数据,分析多条线路故障发生频率的时空分布特点;将故障根据其致因类型分为7大类别,统计分析每类别故障持续时长,探索各类型故障持续时长的分布特征;利用对数正态分布、韦伯分布以及伽马分布拟合各类型故障持续时长,并通过柯尔莫可洛夫-斯米洛夫 (K-S)检验方法对各模型拟合效果进行检验。结果表明:故障频率分布具有明显的时空差异性;对数正态分布是拟合度最好的模型,各类故障持续时长均可认为服从对数正态分布。

关键词: 高速铁路 (HSR), 故障, 时空分布, 持续时长, 对数正态分布

Abstract: Studying the characteristics of disruptions and their duration time is of great significance in improving the advanced disruption prediction and real-time dispatching abilities in HSR. In this paper, historical disruption occurrence records of Chinese HSR in 2014 and 2015 were used to analyze their spatial-temporal distributions. Then, the disruptions were classified into seven clusters according to respective causations, and statistical description of each cluster was conducted. Next, log-normal distribution, Weibull distribution, and Gamma distribution were selected as candidate models to fit in their duration time, and the goodness-of-fit of candidate models were tested while using Kolmogorov-Smirnov (K-S) test. The testing results reveal: the probabilities of disruptions are spatiotemporally different, the log-normal function has the best goodness-of-fit, and the duration time of each classified disruption can be regarded as a random variable following log-normal distribution.

Key words: high-speed railway (HSR), disruption, spatial-temporal distribution, duration, log-normal distribution

中图分类号:

X928.03

黄平, 李忠灿, 文超, 彭其渊. 高速铁路故障时空分布及持续时长分布特征研究^*[J]. 中国安全科学学报, 2018, 28(S2): 99-104.

HUANG Ping, LI Zhongcan, WEN Chao, PENG Qiyuan. Studyonspatial-temporal and duration distribution characteristics of high-speed railway disruptions[J]. China Safety Science Journal, 2018, 28(S2): 99-104.

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高速铁路故障时空分布及持续时长分布特征研究^*

Studyonspatial-temporal and duration distribution characteristics of high-speed railway disruptions

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