Optimization of Ride-Sharing from Public Transportation Hub

Inter-modal transfer efficiency is the key to success for the development of Inter-modal passenger transportation system, such as high-speed rail trains and last-mile transport. Long waiting time often occurs for passengers when they get off trains and transfer to taxi or e-hailing services. On the other hand, taxi and e-hailing service drives also suffer from long waiting time for picking up customers at the over-crowded pickup points. In light of the transfer efficiency challenge, it becomes urgent for transport hub operators (e.g., airports, railway stations) to design systematic approaches to maximize the transfer efficiency between rail trains and last-mile transport. In this paper, we propose a ride-sharing approach for the last-mile transport system while ensuring efficient transfer connection between public transport and last-mile transport. We develop an arc-based mixed integer linear programming model and a set partitioning model with the objective of minimizing total vehicle operating cost and passenger transfer connection cost. Two solution approaches are developed (1) a branch-&-price exact algorithm that can solve real-world size instances to optimality within reasonable computational time; and (2) a column generation heuristic method that yields high-quality near-optimal solutions very efficiently without having to implement a full-scale branch-&-bound search. Computational experiments based on randomly generated test instances confirm the effectiveness and efficiency of the solution approaches. It is also demonstrated based on the real data that ride-sharing is an effective measure to mitigate the crowdedness of the pickup areas and enhance the transfer efficiency for public transport hubs.