Reorganizing Requests in Rural Dial-a-Ride Systems through Needs-Based Scheduling

Abstract

Providing public transport in demand-sparse rural areas means balancing operational efficiency with equitable service for mobility-deprived users. This paper introduces a Needs-Based Dial-aRide Problem (NB-DARP) that integrates behavior models with vehicle routing optimization. Using panel data from rural Japan, we adapt multi-day activity models into a scheduling constraint generation framework to assess user flexibility, and predict availability. Solving the NB-DARP with an Adaptive Large Neighborhood Search (ALNS) metaheuristic, we demonstrate that it outperforms traditional “first come, first served” and ridership-maximizing policies. The proposed framework increases overall service rates and ensures equitable transit access without requiring substantial temporal shifts from users.