Regression model for daily passenger volume of high-speed railway line under capacity constraint
来源期刊：中南大学学报(英文版)2015年第9期
论文作者：LUO Yong-ji LIU Jun SUN Xun LAI Qing-ying
文章页码：3666 - 3676
Key words：high-speed rail; Jinghu high-speed railway (HSR); demand; capacity; forecasting
Abstract: A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway (HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.
LUO Yong-ji(骆泳吉), LIU Jun(刘军), SUN Xun(孙迅), LAI Qing-ying(赖晴鹰)
(State Key Laboratory of Rail Traffic Control and Safety (Beijing Jiaotong University), Beijing 100044, China)
Abstract:A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway (HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.
Key words:high-speed rail; Jinghu high-speed railway (HSR); demand; capacity; forecasting
