Optimizing fleet size and scheduling of feeder transit services considering the influence of bike-sharing systems

Abstract

Abstract This study optimizes the fleet size and schedules of feeder buses that connect metro and residential areas in the context of bike-sharing systems. We propose hybrid operation modes that combine fixed and dynamic frequencies in a bimodal period in comparison with the conventional bus scheduling with constant service frequencies. The effect on endogenous demands from bike-sharing, which is another option for commuters, is considered. This study proposes a multi-objective model under one hybrid mode with morning fixed and evening demand-responsive (MFED) service to minimize the average passenger waiting time and maximize the operator profits. The constraints include vehicle capacity and passenger mode choices. Two algorithms are developed to solve the feeder bus planning problem of a metro station and three nearby communities in Chengdu, China (i.e., non-dominated sorting genetic algorithm-II [NSGA-II] and a customized multi-objective optimization algorithm based on Particle Swarm Optimization [MPSO]). Numerical results show that the proposed MPSO algorithm slightly outperforms NSGA-II in terms of solution quality and efficiency. We further compare two feeder transit operation modes (i.e., morning demand-responsive and evening fixed [MDEF] service and all fixed service) and find that the MFED outperforms MDEF and fixed operation mode in terms of system effectiveness. In addition, we confirm that the effect of bike-sharing systems cannot be neglected because passengers may change their travel mode while waiting for a feeder transit service. This study can provide useful policy implications and operational recommendations for government agencies and transit authorities to regulate the bike-sharing market for effectively addressing the first-and-last-mile issue.