基于贝叶斯网络的高速铁路旅客出行方式预测研究

High-speed Railway Passengers' Travel Mode Prediction based on Bayesian Network

为准确地预测高速铁路综合客运枢纽地区的旅客出行方式比例,以高速铁路旅客出行方式为研究对象,分析高速铁路旅客选择不同交通方式的影响因素,建立基于贝叶斯网络的高速铁路旅客出行方式选择模型。以宜昌东站为例,结合高速铁路旅客出行调查数据,应用SPSSModeler软件对所建立的模型进行验证评估,并对高速铁路旅客选择不同出行方式的比例进行预测。结果表明,该模型能较全面地考虑高速铁路旅客出行选择的影响因素,对预测高速铁路综合客运枢纽地区旅客的出行方式具有较好的适用性,预测精度较高。

It is important for high-speed railway integrated passenger transport hub areas to correctly predict the proportion of travel modes. Taking the travel mode of high-speed railway passengers as the research object and analyzing the factors that influence the high-speed railway passengers＇ choice of different transportation modes, the high-speed railway passengers＇ travel mode selection model is established based on Bayesian network. Taking Yichang East Station as an example, with the use of data of high-speed railway passengers＇ travel and the application of SPSS Modeler software, the established model is validated, and high-speed railway passengers＇ travel mode proportion can be predicted. The result shows that the model can comprehensively consider the influencing factors of high-speed railway passengers＇ travel choice, and it is of great applicability for the way to forecast passengers in high-speed railway station area. At the same time, the prediction accuracy is high.