Decentralized Ride-sharing of Shared Autonomous Vehicles Using Graph Neural Network-Based Reinforcement Learning

Abstract

Ride-sharing has important implications for improving the efficiency of mobility-on-demand systems. However, it remains a challenge due to the complex dynamics between vehicles and requests. This paper presents a decentralized ride-sharing algorithm suitable for shared autonomous vehicles (SAVs) deployment. The ride-sharing problem is formulated as a multi-agent reinforcement learning problem. We explore state representation with the request-vehicle graph to encode shareability and potential coordination information. We use a graph attention network to build a hierarchical structure that unifies ride-sharing assignments with rebalancing and handles real-world scenarios where hundreds of user requests can be associated with vehicles. We show results in both generic grid-world and SUMO simulation with real-world data from the Manhattan area. We empirically demonstrate that our proposed approach can achieve similar performance compared with a state-of-the-art centralized optimization method and higher computation efficiency.