Fare Structure Optimization of Intercity Railway With Regional Passenger Income Disparities

ABSTRACT The fare structure of intercity railways is crucial for passenger service and sustainable operation. The Current distance‐based fare structure (CFS) and a regional distance‐based fare structure (RDFS) are investigated to achieve breakeven under limited subsidies. Passenger choice behaviors are measured by a nested‐logit (NL) model with prospect theory, considering regional passenger income disparities. A nonlinear programming model is proposed to determine the fare structure and departure headway, of which the objective function maximizes the social welfare, including operator revenue and passenger turnover. A multiple‐population genetic algorithm with an elite strategy is designed to solve the problem. The case study shows that the proposed RDFS can effectively increase operator revenue by 5.88%, while sacrificing only 1.73% of the turnover compared to the current fare structure. Under limited subsidies, the RDFS reduces the required passenger demand by 5.70% for achieving breakeven. RDFS consistently shows the highest social welfare under varying passenger income levels among cities and commuter proportions.