Ridesharing Methods for High-Speed Railway Hubs Considering Path Similarity

Abstract

We propose a hub ridesharing method that considers path similarity to swiftly evacuate high volumes of passengers arriving at a high-speed railway hub. The technique aims to minimize total mileage and the number of service vehicles, considering the characteristics of hub passengers, such as the constraints of large luggage, departure times, and arrival times. Meanwhile, to meet passengers’ expectations, a path morphology similarity indicator combining directional and locational features is developed and used as a crucial criterion for passenger matching. A two-stage algorithm is designed as a solution. Passenger requests are clustered based on path vector similarity in the first stage using a heuristic approach. In the second stage, we employ an adaptive large-scale neighborhood search to form passenger matches and shared routes. The experiments demonstrate that this method can reduce operational costs, enhance computational efficiency, and shorten passenger wait times. Taking path similarity into account significantly decreases passenger detour distances. It improves the Jaccard coefficient (JAC) of post-ridesharing paths, fulfilling the passenger’s psychological expectation that the shared route will closely resemble the original one.