High-speed railway passenger flow equilibrium among trains of common lines based on travel behavior analysis under dynamic pricing

This paper aims to make contributions to balancing the passenger flow among high-speed railway trains of common lines as well as raise revenue via a behavioral-based optimal dynamic pricing. Based on an HSR trip choice survey, a set of Nested-logit (NL) models is built to analyze passengers' preferences for different factors under various scenarios. The upper level belongs to the departure time period while the lower level belongs to specific trains with different levels of service. Given the conditions of fixed transportation capability, an optimization model is built to optimize the pricing among trains of common lines. The NL model results show that the time values of round trips are greater than that of one-way trips, and the time values of business trips are greater than that of leisure trips. Besides, the optimization model is verified to be capable of raising revenue, improving passenger flow equilibrium, and inducing extra passenger demand.