摘要
为保障高速铁路列车按图行车,降低潜在的行车冲突风险,利用列车运行实绩研究高速铁路列车晚点及其传播特性。首先,通过描述性统计分析广深高铁列车运行实绩数据,得到广深高铁列车运行的初始晚点分布;然后,运用层次聚类算法聚类分析晚点列车,得到4类晚点列车序列,并提取重要的晚点特征变量;最后,基于随机森林模型预测各类晚点列车序列的晚点时间。研究结果表明:结合随机森林模型预测4类晚点列车晚点时间的准确度达到了84%以上。
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%.
作者
胡瑞
徐传玲
冯永泰
文超
王全泉
HU Rui;XU Chuanling;FENG Yongtai;WEN Chao;WANG Quanquan(National United Engineering Laboratory of Integrated and Intelligent Transportation,Southwest Jiaotong University,Chengdu Sichuan 610031,China;National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Southwest Jiaotong University,Chengdu Sichuan 610031,China;Railway Research Center,University of Waterloo,Waterloo N2L3G1,Canada;Transport Department,China Railway Guangzhou Group Co.,Ltd.,Guangzhou Guangdong 510088,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2019年第S02期181-186,共6页
China Safety Science Journal
基金
国家自然科学基金资助(71871188,61503311)
四川省科技厅应用基础研究项目(2018JY0567).
关键词
高速铁路
列车晚点
数据驱动
层次聚类
随机森林模型
high-speed railway
train delays
data driven
hierarchical clustering
random forest model
作者简介
胡瑞(1995—),男,四川成都人,硕士研究生,主要研究方向为铁路运输组织优化。E-mail:791257593@qq.com;文超,副教授
