2020/07/14 by Yuntian Deng, Hao Chen, Deng, Yuntian +9
Engineering · Social Sciences · #Electric Vehicles and Infrastructure #FOS: Computer and information sciences #FOS: Electrical engineering #Social and Information Networks (cs.SI) #Systems and Control (eess.SY) #Transportation Planning and Optimization #Transportation and Mobility Innovations #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2007.06801
openalex publication_date 2020/07/14 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
The problem of designing a rebalancing algorithm for a large-scale ridehailing system with asymmetric demand is considered here. We pose the rebalancing problem within a semi Markov decision problem (SMDP) framework with closed queues of vehicles serving stationary, but asymmetric demand, over a large city with multiple nodes (representing neighborhoods). We assume that the passengers queue up at every node until they are matched with a vehicle. The goal of the SMDP is to minimize a convex combination of the waiting time of the passengers and the total empty vehicle miles traveled. The resulting SMDP appears to be difficult to solve for closed-form expression for the rebalancing strategy. As a result, we use a deep reinforcement learning algorithm to determine the approximately optimal solution to the SMDP. The trained policy is compared with other well-known algorithms for rebalancing, which are designed to address other objectives (such as to minimize demand drop probability) for the ridehailing problem.