摘要
列车晚点预测及分析直接服务于高速铁路调度指挥,是列车晚点研究的重点内容.通过对列车晚点传播结构及传播规律的分析,提出了一种高速铁路列车连带晚点的智能预测及特征识别方法.首先利用列车晚点自身传播及相邻线列车晚点传播的关系,构建基于小波神经网络的列车连带晚点递阶预测模型;然后利用列车晚点波动的线性组合方程及其结构向量,进行列车连带晚点影响值的量化;最后综合连带晚点的实际值、预测值和影响值,将晚点分为严重晚点、消散晚点、潜在晚点、一般晚点四种类型.成渝高铁的实例数据表明,小波神经网络的预测结果具有较高精度,连带晚点的分类结果也比较符合实际,能够为高速铁路列车连带晚点的运行调整提供数据支撑.
The prediction and analysis of train delays is the core content of train delay research,which directly serves the dispatching command of high-speed railway.Based on the propagation structure and law of train delay analyzing,an intelligent prediction and feature recognition method for the large area joint train delay is proposed.Firstly,considering the relationship between train delay propagation and adjacent train delays propagation,a hierarchical prediction model of train delay is constructed using wavelet neural network.Secondly,the linear combination equation of train delay fluctuation and its structural vector are used to quantify the impact value of train delays.Finally,combined with the prediction value and impact value of continuous delays,the joint delay are divided into heavy delay,dissipation delay,potential delay and general delay.The example using the data of Chengdu-Chongqing high-speed railway shows that the prediction results of wavelet neural network have high precision,and the classification results of joint delay are more realistic,which can provide data support for large-area train delayed operation adjustment.
作者
张琦
陈峰
张涛
袁志明
ZHANG Qi;CHEN Feng;ZHANG Tao;YUAN Zhi-Ming(Signal and Communication Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081)
出处
《自动化学报》
EI
CSCD
北大核心
2019年第12期2251-2259,共9页
Acta Automatica Sinica
基金
国家自然科学基金(61790575)
中国国家铁路集团有限公司科技研究开发计划课题(N2019G020)
中国铁道科学研究院集团有限公司科研课题(2018YJ070,2018YJ076)资助~~
关键词
铁路运输
高速铁路
列车连带晚点
晚点预测
特征识别
神经网络
Railway transportation
high-speed railway
train joint delay
delay prediction
feature recognition
neural network
