铁道科学与工程学报2025，Vol.22Issue(5)：1936-1948,13.DOI:10.19713/j.cnki.43-1423/u.T20241145
基于CNN-Informer的ATO列车停车曲线预测研究
ATO train stop curve prediction based on CNN-Informer
摘要
Abstract
The accuracy of inbound stop target is one of the important indexes to measure the performance of ATO.The realization of accurate parking can not only ensure the safety of train operation and the comfort of passengers,but also directly affect the operation efficiency.In order to accurately predict ATO stopping track of high-speed railway,an improved CNN-Informer ATO stopping benchmark model was proposed in the paper.The model could deeply dig the multi-dimensional data in the train operation process.It could take the train operation data as input,covering the key features such as slope,mileage and operating conditions.Through the CNN convolutional layer and pooled layer,the model can automatically learn and extract the core features in the input data,thereby improving the accuracy of the prediction.The feature vector output of CNN and the fully connected layer was used as the input of Informer long time series prediction algorithm to predict ATO parking benchmark.In order to verify the performance of the model,thesis used the real ATO control data of a high-speed railway line for training,verification and testing.The test results show that the ATO parking benchmarking model proposed in this paper can effectively predict the train control situation before the train enters the station,and assist the ATO system to adjust the train control strategy in time.Compared with the traditional Informer model,the mean square error is reduced by 4.95%,the average absolute error is reduced by 22.35%,and the average absolute percentage error is reduced by 4.16%when predicting the ATO controlled vehicle speed.When predicting the station distance,the mean square error decreases by 21.83%,the mean absolute error decreases by 19.69%,and the mean absolute percentage error decreases by 53.72%.This study not only provides theoretical support for the accuracy of ATO stopping target of high-speed railway,but also provides a new idea and method for the study of train stopping process in the future.关键词
高速铁路/列车自动驾驶/停车对标/深度学习/CNN-InformerKey words
high-speed railway/autonomous train operation/stop check mark/deep learning/CNN-Informer分类
交通运输引用本文复制引用
王心仪,程剑锋,易海旺..基于CNN-Informer的ATO列车停车曲线预测研究[J].铁道科学与工程学报,2025,22(5):1936-1948,13.基金项目
北京市科技计划项目(Z231100003823033) （Z231100003823033）
中国铁道科学研究院集团有限公司课题(2023YJ105) （2023YJ105）
北京华铁信息技术有限公司科研项目(2023HT06) （2023HT06）
