Long-distance line planning with endogenous demand

Abstract

This study develops an integrated line planning framework for long-distance rail by treating passenger demand as endogenous, recognizing that both ridership level and distribution depend on service frequency and travel time. To address station heterogeneity, the methodology employs latent-class clustering to group stations into probabilistic typologies, which informs a gravity-based demand model. This is integrated into a mixed-integer linear program that builds upon hierarchical line generation and optimizes service planning while accounting for frequency-dependent transfer times. Applying this framework to the rail network of Great Britain, we conclude that demand-aware planning significantly enhances economic efficiency and service relevance compared to benchmark line generation methods and traditional fixed-demand models, respectively. Our findings demonstrate that capturing travellers’ behavioural responses and structural roles of network stations leads to more realistic, cost-effective service designs that better align infrastructure supply with passenger needs.