Bus Pooling: A Large-Scale Bus Ridesharing Service

Abstract

Ridesharing, a shared service that uses the information and knowledge matching, can efficiently utilize scattered social resources to reduce the demand for vehicles in urban road networks. However, car ridesharing has the problems of low capacity and high cost, and it cannot satisfy demands for recurring, long-distance, and low-cost trips. In this paper, we formally define the bus ridesharing problem and propose a large-scale bus ridesharing service to resolve this problem. In our proposed model, the rider can use an online bus-hailing service to upload his or her trip demand and wait to be picked up when it gathers enough people. The provider assigns drivers to riders after integrating the matched ride requests. To maximize ridesharing’s success rate, we developed both exact algorithms and approximate algorithms to optimize the ride-matching service. A real-life dataset that contains 65,065-trip instances extracted from 10,585 Shanghai taxis from one day (Apr 1, 2018) is used to demonstrate that our proposed service can provide higher cost performance and on-demand bus services for every ride request. Meanwhile, it reduces the number of vehicles used by 92% and 96% and the amount of oil used by 87% and 92% compared with car ridesharing and no ridesharing, respectively.