京沪高铁列车运行晚点预测方法研究

为快速、准确地掌握列车的运行状态及未来的运行趋势，需要对列车运行晚点预测方法进行深入研究。文章根据对北京—上海高速铁路（简称：京沪高铁）2020年列车运行数据的分析，包括停站时长对于晚点的影响及不同初始晚点时长下的传播车站数，提出了基于循环神经网络（RNN,Recurrent Neural Network）的全段预测方法，使用同步多对多模式的RNN模型作为基础模型结构，建立列车运行晚点预测模型。在特征值的选择上，采用集成梯度打分法，从多个特征值中选择12个最显著的变量作为模型自变量。采用该模型对京沪高铁2020年晚点数据进行验证，结果表明，该模型在验证集上5 min的误差范围内可以达到89%的准确率，该预测方法可以满足实际生产的需要，有助于调度部门进行科学决策，有利于提升铁路旅客服务质量。

Prediction method of train operation delay of Beijing-Shanghai High-speed Railway

In order to quickly and accurately grasp the train operation status and future operation trend, it is necessary to deeply study the prediction method of train operation delay. Based on the analysis of the train operation data of Beijing-Shanghai High-speed Railway in 2020, including the impact of dwell time on delay and the number of propagation stations under different initial delay time, this paper put forward a full section prediction method based on Recurrent Neural Network(RNN), and used the RNN model of synchronous many to many mode as the basic model structure, established a prediction model of train operation delay. In the selection of eigenvalues, the paper used the integrated gradient scoring method to select the 12 most significant variables from multiple eigenvalues as the independent variables of the prediction model, and verified the delay data of Beijing-Shanghai High-speed Railway in 2020 by the prediction model. The verification results show that the model can achieve 89% accuracy within the error range of 5 minutes on the verification set. The prediction method can meet the needs of actual production, help the dispatching department make scientific decisions and improve the quality of railway passenger service.