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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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Author(s)

  • Enshu Wang
  • Songrong Lin
  • Feng Shen
  • Tianyu Bao
  • Jun Zhang
  • Adel W. Sadek
  • Chunming Qiao

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)5313-5325
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number5
Online published26 Jan 2023
Publication statusPublished - May 2023

Abstract

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. © 2023 IEEE.

Research Area(s)

  • Decision making, Delays, Games, graph attention network, multi-agent reinforcement learning, options framework, Public transportation, Reinforcement learning, Training, Urban areas, Urban bus system

Citation Format(s)

Double Graph Attention Actor-Critic Framework for Urban Bus-Pooling System. / Wang, Enshu; Liu, Bingyi; Lin, Songrong et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 24, No. 5, 05.2023, p. 5313-5325.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review