RSTR: A Two-Layer Taxi Repositioning Strategy Using Multi-Agent Reinforcement Learning

Ride-hailing platforms allow people to request rides, but sometimes they do not respond quickly enough. Due to the complex urban transportation system and the large geographical area of the city, the imbalance between supply and demand becomes a common problem. To solve this problem, many studies have proposed taxi repositioning methods. However, each of two mainstream methods (region-based and taxi-based) has its own advantages and disadvantages. In this paper, we propose the RSTR model, which is a two-layer model that takes advantages of the two mainstream methods. We model the problem as a partially observable Markov decision process and design the optimization objective. To generate more accurate repositioning strategies and improve model training outcomes, we propose a many-to-many scheduling mode and demonstrate its effectiveness. Extensive experiments on real-world datasets show that RSTR can effectively balance supply and demand and outperform other baseline methods.