Resilience-Oriented Line Planning for Multimodal Rail Transit Network Considering Uncertain Passenger Service Choice Behavior

Abstract

The train line planning problem (LPP) determines passenger travel path accessibility by optimizing train routes and stop plans. This study considers the uncertainty of passenger service choice behavior and the partial periodic operation pattern in the multimodal rail transit network (MRTN), defines system resilience as the network’s ability to resist interference, and constructs a resilience-oriented LPP model with constraints for passenger assignment that account for uncertain service choice behavior. A customized iterative solution procedure is designed to solve this model. In each iteration, a passenger assignment algorithm that integrates an available travel path search method is developed to determine passenger travel paths, and an improved adaptive large neighborhood search (IALNS) algorithm with a network decomposition strategy is designed to solve the LPP. The proposed approach is examined on Shanghai MRTN, with analysis of influences of travel path resilience requirement, uncertainty in passenger service choice behavior, and partial periodic operation strategy. The results indicate that incorporating path resilience into LPP can enhance network resilience with limited operational cost increases, accounting for passenger uncertain service choice behavior can more accurately match the transport capacity with passenger demand, and the partial periodic pattern can balance the regularity and flexibility of the line plan. Furthermore, the IALNS algorithm outperforms Gurobi on large-scale cases, and the proposed approach can well balance operational costs, generalized passenger travel time, and network resilience. Case study findings provide insights for rail operators in line planning.