Optimization of ride-sharing with passenger transfer via deep reinforcement learning

With the emergence of the sharing economy and the rapid growth of mobile communications technologies, many novel sharing service models have been developed stemming from ride-hailing. Urban traffic congestion, coupled with energy conservation and emissions reduction, has prompted research on enhancing vehicle seat utilization in taxi service. To offer more effective and reliable ride-hailing, we consider ride-sharing problem with passenger transfer that allows passegers to transfer between vehicles at transfer stations. The problem requires simultaneous addressing the issues of request dispatching, transfer scheduling, and vehicle rebalancing. Studying such a ride-hailing model, we propose a novel joint decision framework combining deep reinforcement learning (DRL) with integer-linear programming (ILP) to solve the problem. We use ILP to obtain the optimal online dispatching and matching strategy in each decision stage, and DRL to learn the approximate state value of each vehicle that incorporates with some strategies to limit the state space and reduce the computational complexity. Performing numerical studies on the real-world trip dataset in Chengdu, we demonstrate that the proposed method outperforms several state-of-the-art methods, and that ride-sharing with passenger transfer is more beneficial than traditional ride-sharing.