Study on high-speed railway disruption classification and model of its influence on train number

doi:10.16265/j.cnki.issn1003-3033.2018.S2.009

China Safety Science Journal ›› 2018, Vol. 28 ›› Issue (S2): 46-53.doi: 10.16265/j.cnki.issn1003-3033.2018.S2.009

• Safety Systematology • Previous Articles Next Articles

Study on high-speed railway disruption classification and model of its influence on train number

HUANG Ping^1,2,3, PENG Qiyuan^1,2, WEN Chao^1,2,3, LI Zhongcan^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 Railway Research Centre, University of Waterloo, Waterloo N2L3G1, Canada

Received:2018-08-29 Revised:2018-11-10 Online:2018-12-30 Published:2020-11-11

Abstract

Abstract: It is of great significance for the influence of high-speed railway disruption to be accurately measured forimproving the real-time dispatching and management abilities of HSR. First, based on the historical train operation records of Wuhan-Guangzhou HSR, delayed train groups caused by disruptions were extracted and a K-Means clustering algorithm was used to classify them into four categories, according to disruption and timetable characteristics. Then, five distribution models were investigated to fit the distributions of the affected train number in different clustered categories. Next, optimal distribution models for the number of affected trains were selected according to Kolmogorov-Smirnov (K-S) test. Finally, verification results show that distributions of the affected train numberin the test dataset were consistent with the fitted optimal distribution models. The fitted models can be used to estimate the number of trains affected by disruptions in real-time dispatching process of HSR.

Key words: high-speed railway (HSR), disruption, affected trains number, K-Means algorithm, distribution model

CLC Number:

X951

HUANG Ping, PENG Qiyuan, WEN Chao, LI Zhongcan. Study on high-speed railway disruption classification and model of its influence on train number[J]. China Safety Science Journal, 2018, 28(S2): 46-53.

References

[1] 彭其渊, 李建光, 杨宇翔, 等. 高速铁路建设对我国铁路运输的影响 [J]. 西南交通大学学报, 2016, 51(3): 525-533.
PENG Qiyuan, LI Jianguang, YANG Yuxiang, et al. Influences of high-speed railway construction on railway transportation of China[J]. Journal of Southwest Jiaotong University, 2016,51(3): 525-533.
[2] 文超, 彭其渊, 陈芋宏. 高速铁路列车运行冲突判定及其实现 [J]. 西南交通大学学报, 2012, 47(5): 867-873.
WEN Chao, PENG Qiyuan, CHEN Yuhong. Judgment of train operation conflict and its implementation for high-speed railway [J]. Journal of Southwest Jiaotong University, 2012, 47(5): 867-873.
[3] 刘健, 孟学雷, 王金霞. 突发事件下的列车运行图稳定性分析 [J]. 铁路计算机应用, 2015, 24(9): 1-5.
LIU Jian, MENG Xuelei, WANG Jinxia. Stability analysis of train diagram in emergency[J]. Railway Computer Application, 2015, 24(9): 1-5.
[4] 黄平, 彭其渊, 文超, 等. 武广高速铁路列车晚点恢复时间预测的随机森林模型 [J]. 铁道学报, 2018, 40(7): 1-9.
HUANG Ping, PENG Qiyuan, WEN Chao, et al. Random forest prediction model for Wuhan-Guangzhou HSR primary train delays recovery [J]. Journal of the China Railway Society, 2018, 40(7): 1-9.
[5] HUISMAN T, BOUCHERIE R J. Running times on railway sections with heterogeneous train traffic [J]. Transportation Research Part B: Methodological, 2001, 35(3): 271-292.
[6] CORMAN F, D'ARIANO A, HANSEN I A. Evaluating disturbance robustness of railway schedules [J]. Journal of Intelligent Transportation Systems, 2014, 18(1): 106-120.
[7] WENG J, ZHENG Y, QU X, et al. Development of a maximum likelihood regression tree-based model for predicting subway incident delay [J]. Transportation Research Part C: Emerging Technologies, 2015, 57: 30-41.
[8] ZILKO A A, KUROWICKA D, GOVERDE R M. Modeling railway disruption lengths with Copula Bayesian networks [J]. Transportation Research Part C: Emerging Technologies, 2016, 68: 350-368.
[9] 范正日. 我国铁路重特大事故发生规律与特征研究 [J]. 中国安全科学学报, 2018, 28(增1): 135-140.
FAN Zhengri. Cycle analysis and characteristics for the occurrence of major railway accidents in China [J]. China Safety Science Journal, 2018, 28(S1): 135-140.
[10] YUAN J. Stochastic modelling of train delays and delay propagation in stations [D]. Delfe: Delft University of Technology, 2006.
[11] BRIGGS K, BECK C. Modelling train delays with q-exponential functions [J]. Physica A: Statistical Mechanics and its Applications, 2007, 378(2): 498-504.
[12] XU P, CORMAN F, PENG Q. Analyzing railway disruptions and their impact on delayed traffic in Chinese high-speed railway [J]. IFAC-Papers On Line, 2016, 49(3): 84-89.
[13] WEN C, LI Z, LESSAN J, et al. Statistical investigation on train primary delay based on real records: evidence from Wuhan-Guangzhou HSR [J]. International Journal of Rail Transportation, 2017, 5(3): 170-189.
[14] 中国铁路总公司. 非正常情况下高速铁路调度指挥应急处置案例 [M]. 北京:中国铁道出版社, 2013:1-66.
[15] HARTIGAN J A, WONG M A. Algorithm AS 136: a k-means clustering algorithm [J]. Journal of the Royal Statistical Society Series C :Applied Statistics:, 1979, 28(1): 100-108.
[16] ROSS S M. Introduction to probability models 11^th edition [M]. New York: Academic Press, 2014: 21-33
[17] MASSEY JR F J. The Kolmogorov-Smirnov test for goodness of fit [J]. Journal of the American Statistical Association, 1951, 46(253): 68-78.

Study on high-speed railway disruption classification and model of its influence on train number

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