Short-term train arrival delay prediction:a data-driven approach  

作　　者：Qingyun Fu Shuxin Ding Tao Zhang Rongsheng Wang Ping Hu Cunlai Pu 

机构地区：[1]Postgraduate Department,China Academy of Railway Sciences,Beijing,China [2]Signal and Communication Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing,China [3]Traffic Management Laboratory for High-Speed Railway,National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit,Beijing,China [4]Scientific and Technological Information Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing,China [5]Yibin Track,Signal and Communication Depot,China Railway Chengdu Group Co.,Ltd,Chengdu,China [6]School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing,China

出　　处：《Railway Sciences》2024年第4期514-529,共16页铁道科学（英文）

基　　金：supported in part by the National Natural Science Foundation of China under Grant 62203468;in part by the Technological Research and Development Program of China State Railway Group Co.,Ltd.under Grant Q2023X011;in part by the Young Elite Scientist Sponsorship Program by China Association for Science and Technology(CAST)under Grant 2022QNRC001;in part by the Youth Talent Program Supported by China Railway Society,and in part by the Research Program of China Academy of Railway Sciences Corporation Limited under Grant 2023YJ112.

摘　　要：Purpose-To optimize train operations,dispatchers currently rely on experience for quick adjustments when delays occur.However,delay predictions often involve imprecise shifts based on known delay times.Real-time and accurate train delay predictions,facilitated by data-driven neural network models,can significantly reduce dispatcher stress and improve adjustment plans.Leveraging current train operation data,these models enable swift and precise predictions,addressing challenges posed by train delays in high-speed rail networks during unforeseen events.Design/methodology/approach-This paper proposes CBLA-net,a neural network architecture for predicting late arrival times.It combines CNN,Bi-LSTM,and attention mechanisms to extract features,handle time series data,and enhance information utilization.Trained on operational data from the Beijing-Tianjin line,it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.Findings-This study evaluates our model’s predictive performance using two data approaches:one considering full data and another focusing only on late arrivals.Results show precise and rapid predictions.Training with full data achieves aMAEof approximately 0.54 minutes and a RMSEof 0.65 minutes,surpassing the model trained solely on delay data(MAE:is about 1.02 min,RMSE:is about 1.52 min).Despite superior overall performance with full data,the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals.For enhanced adaptability to real-world train operations,training with full data is recommended.Originality/value-This paper introduces a novel neural network model,CBLA-net,for predicting train delay times.It innovatively compares and analyzes the model’s performance using both full data and delay data formats.Additionally,the evaluation of the network’s predictive capabilities considers different scenarios,providing a comprehensive demonstration of the model’s predictive performan

关 键 词：Train delay prediction Intelligent dispatching command Deep learning Convolutional neural network Long short-term memory Attention mechanism 

分 类 号：U29[交通运输工程—交通运输规划与管理]