基于组合预测模型的高铁预售期购票量预测研究

Prediction of Ticket Purchase Volume in Pre-Sale Period of High-Speed Railway Based on Combined Prediction Model

随着中国交通强国战略的部署,高速铁路网络正持续扩展,铁路旅客出行的需求量稳步提升。面对庞大的高铁客运市场,如何运用智能高效的深度学习组合预测模型,融合多种购票影响特征因素,实时掌握预售期各天旅客购票需求的变化情况,从而为铁路部门提供高效可靠的旅客车票预售期购票量预测模型成为亟需解决的问题。以沪昆高铁线路上OD (origin-destination)间高铁旅客历史购票数据为实例,考虑历史连续发车日期预售期各天购票量、发车日期的日期、节假日和季节特征属性,构建了基于深度学习CNN-LSTM的高铁预售期购票量组合预测模型。研究表明,基于深度学习CNN-LSTM的组合预测模型相较于参数模型和机器学习模型预测性能较佳,为铁路客运市场动态调整票额提供了相关理论参考。

With the deployment of China′s transportation power strategy,the high-speed railway network is continuing to expand.The demand for railway passenger travel is steadily increasing.Facing the huge high-speed railway passenger transport market,how to use intelligent and efficient deep learning technology to build a combined prediction model is a challenge.This paper integrates a variety of factors affecting ticket purchases and is committed to explore the changes in passenger ticket purchase demand on each day of the pre-sale period in real time.This shows that it′s becoming an urgent problem to provide an efficient and reliable prediction model for passenger ticket pre-sale period for the railway department.This paper takes the historical ticket purchase data of high-speed railway passengers between OD(origin-destination)on the Shanghai-Kunming high-speed railway line as an example.The combined prediction model considers the number of tickets purchased on each day of the pre-sale period of the historical continuous departure date,the date of the departure date,statutory holiday,and seasonal characteristics.The research shows that the combined prediction model based on deep learning CNN-LSTM has better predictive power than the parameter model and machine learning model,which provides a theoretical reference for the dynamic adjustment of fares in the railway passenger transport market.