Fare Structure Optimization of Intercity Railway With Regional Passenger Income Disparities

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.