Optimization of Train Timetables in High-Speed Railway Corridors Considering Passenger Departure Time and Price Preferences

In a high-speed railway (HSR) corridor, railway companies provide diverse train services with different features such as departure time, running speed, and fare. Furthermore, passengers have varying preferences, which influence their travel choices. Focusing on this problem, this paper proposed a novel train timetable optimization model based on departure time and pricing strategy to analyze their impact on passengers’ travel choices with a consistent operating speed. Additionally, an extended time-space-fare network is constructed to visualize this process. The generalized cost of each arc in the network is used as a criterion for evaluating passenger selection. Then, we adapted a passenger flow distribution method based on the user equilibrium (UE) principle to allocate the passenger flow to each train. We also developed a bi-level planning model with passenger allocation as the lower-level model and timetable decision as the upper-level one to optimize collaboratively. Finally, a genetic algorithm integrated with the Frank-Wolfe method is designed to seek the optimal timetable and pricing strategy and a concrete numerical example in Lanzhou-Xi’an HSR is taken to investigate the effectiveness of the proposed model and algorithm.