An Integrated Model for Demand Forecasting and Train Stop Planning for High-Speed Rail

Abstract

Studying the interaction between demand forecasting and train stop planning is important, as it ensures the sustainable development of high-speed rail (HSR). Forecasting the demand for high-speed rail (HSR), which refers to modal choice or modal split in this paper, is the first step in high-speed rail (HSR) planning. Given the travel demand and the number of train trips on each route, the train stop planning problem (TSPP) of line planning involves determining the stations at which each train trip stops, i.e., the stop-schedule of each train trip, so that the demand can be satisfied. To integrate and formulate the two problems, i.e., the modal choice problem (MCP) and train stop planning problem (TSPP), a nonlinear model is presented with the objective of maximizing the total demand captured by a high-speed rail system. To solve the model, a heuristic iterative algorithm is developed. To study the relationship between the demand and the service, the Beijing–Shanghai high-speed rail (HSR) corridor in China is selected. The empirical analysis indicates that combining modal choice and train stop planning should be considered for the sustainable design of high-speed rail (HSR) train services. Furthermore, the model simulates the impact of the number of stops on its mode share by reflecting changes in travelers’ behaviors according to HSR train stop planning, and it also provides a theoretical basis for the evaluation of the adaptability of the service network to travel demand.