Predicting the passenger booking volume distribution during pre-sale period for high-speed railways before pre-sale(31 days apart)is the premise for accurate revenue management by railway enterprises.Based on the pre-sale mode of HSR and passenger ticket sales records,this paper analyzed the correlation of booking volume on each pre-sale day during the pre-sale period,and explored the influencing factors of passenger booking volume distribution during pre-sale period.A multi-output least squares support vector regression-convolutional long short-term memory network(MLSSVR-ConvLSTM)model considering the correlation between multiple outputs was constructed,taking into account the departure date characteristics and the temporal characteristics of passenger booking volume distribution.Taking the OD passengers under three different distances from Shanghai Hongqiao Station to Beijing South Station,Shanghai Hongqiao Station to Xuzhou East Station,and Shanghai Hongqiao Station to Wuxi East Station in the Beijing-Shanghai HSR line as examples,the empirical analysis of the prediction of passenger booking volume distribution during pre-sale period was conducted.The results show that MLSSVR-ConvLSTM model prediction results can reflect the change trend of the real passenger booking volume distribution during pre-sale period,with the mean absolute percentage error ranging from 6.7%to 11.0%.The prediction effect is better than multiple linear regression,K-nearest neighbor regression,extreme gradient boosting,support vector regression machine,multiple output least squares support vector regression,and convolutional long short-term memory network models,which verifies the reasonableness and validity of the proposed model.It further shows that when constructing the prediction model for passenger booking volume distribution during pre-sale period,considering the integrity of passenger booking volume distribution during pre-sale period and the comprehensive influence of various factors can effectively improve the prediction accuracy of the model.The proposed model for prediction of passenger booking volume distribution during pre-sale period can provide theoretical support for railway enterprises to formulate policies such as dynamic ticket allocation and floating fares.
关键词
高速铁路/预售期/旅客购票量分布预测/MLSSVR-ConvLSTM模型/售票数据
Key words
high-speed railways/pre-sale period/prediction of passenger booking volume distribution/MLSSVR-ConvLSTM model/ticket sales records
维普
万方数据