Fare system selection and line planning for new suburban rail lines considering the supply–demand interactions

For newly constructed suburban railway lines, the lack of historical passenger flow data presents a significant challenge in designing line plans and selecting an appropriate fare system. To address this issue, a least-squares procedure is employed to estimate both a gravity model and a polynomial model, which capture the interaction between passenger demand and supply factors for the existing railway network in the same region as the new lines, using geographic data, socioeconomic data, historical passenger flow, and train operation data. These models are then utilized to capture the interdependencies between demand and supply for the new suburban railway lines. An integrated line planning optimization framework is proposed that incorporates either the gravity or polynomial model for demand estimation to optimize line plan and fare system selection considering the impact of travel time and ticket costs on passenger demand. In this study, the estimated gravity and polynomial models express demand as a function of generalized travel time and ticket costs, these factors are closely tied to both line planning and fare system selection decisions. This enables the line planning optimization model to flexibly consider scenarios where newly constructed railway corridors implement different line-setting and different fare system configurations, while also evaluating the contribution of specific strategy attributes to demand generation and simultaneously addressing demand allocation. The proposed method has been tested using Shanghai's urban and suburban lines. Historical data from the entire Shanghai subway network were used to analyze the interdependencies between demand and supply, which were then applied to two new lines – the Shanghai Jiading–Minhang Line and the Airport Link Line – to assist decision-making regarding line setting and fare system selection. The computational results indicate that the accuracy of demand estimation has significantly improved by incorporating both supply-side and demand-side factors. Additionally, embedding these demand–supply interdependencies into the line planning optimization model has greatly enhanced the ability of train operation solutions to balance passenger demand with capacity supply.