高速铁路列车晚点时间实时预测的神经网络模型

A neural network model for real-time prediction of high-speed railway delays

准确地预测高速列车晚点时间对提高高速铁路实时调度指挥水平及运输服务质量有重要意义。以武汉-广州高速铁路(HSR)列车运行实绩数据为基础,建立基于循环神经网络(RNN)的列车晚点预测模型。该模型中,按照列车实际运行顺序输入RNN以利用其反馈机制学习到相邻列车间相互作用关系。基于平均绝对误差(MAE)以及平均绝对百分误差(MAPE)评估模型的预测能力。结果表明:提出的深度学习模型预测精度明显高于人工神经网络、支持向量回归及马尔科夫等已有列车晚点时间预测模型。

Accurately forecasting train delays has great significance for improving the real-time dispatching ability and the quality of transport service.By using the train operation records of Wuhan-Guangzhou HSR,a deep learning model based on RNN was established to predict train delays.In this model,the trains were fed into RNN according to their operating orders to use the feed-back mechanism of RNN to capture train interaction.The model performance was evaluated based on mean absolute error(MAE)and mean absolute percentage error(MAPE)metrics.The results demonstrate that deep learning model proposed in this paper distinctly outperform other models widely used delay prediction,including Artificial Neural Network,Support Vector Regression,and Markov Model.