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

In a high-speed railway (HSR) corridor, diverse train services are provided by railway companies. These different trains’ features own various attributes (e.g., departure time, running speed, and fare) being the basis and focus for passengers deciding their travel. Besides, passenger’s varying preference always leads to a diversity of their decision. Focusing on this problem, this paper proposed a train timetable optimization model based on departure time and pricing strategy to further analyze their impact on passengers' travel choices with a consistent operating speed, and to construct an extended time-space-fare network to visualize this process. The generalized cost of each arc in the network will be applied as a criterion for evaluating passenger selection. Then, we adapt a passenger flow allocation method based on the user equilibrium (UE) principle to allocate passenger flow to each train. Finally, a bi-level planning model is designed, where the passenger allocation is performed as the lower-level model, and the timetable decision is the upper-level one to optimize collaboratively. Moreover, 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 both the model and the algorithm