Double Graph Attention Actor-Critic Framework for Urban Bus-Pooling System

To unleash the power of buses, we propose a bus-pooling system that keeps the notion of bus stops and terminals but discards the concept of fixed bus lines by enabling buses to choose the next stop or terminal based on orders submitted by passengers. Each bus, unlike a taxi, must consider the additional delays experienced by the passengers already on board when deciding how to adapt its route to serve new orders. This paper treats each bus as an agent and formulates the buses' re-routing decision-making process as a Semi-Markov game. Then, we propose a novel double graph attention actor-critic (DGAAC) framework by integrating high-level and low-level actor-critics separately with graph attention networks (GATs) to solve the game. Specifically, GATs embedded in high-level and low-level critics take a large-scale graph covering a city-scale area as input and capture graph-structured mutual influences among buses. In contrast, the high-level and low-level actors equipped with GATs only take the n-hop sub-graph with local information as the input and are employed as the distributed decision module of each bus. We conduct extensive experiments on one of the largest real-world datasets in Shenzhen, China, and validate that the proposed DGAAC framework greatly outperforms all baselines.