Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control

TL;DR

This paper introduces a novel multi-agent reinforcement learning framework that models spatio-temporal dependencies with hypergraph attention to optimize traffic signals for multimodal corridor networks, emphasizing public transportation.

Contribution

It proposes a scalable dual-stage hypergraph attention mechanism within a MARL framework for human-centric traffic signal control, incorporating hybrid action spaces for adaptive decision-making.

Findings

Improves multimodal traffic performance in corridor scenarios

Achieves superior results compared to baseline methods

Ablation studies highlight the importance of temporal hyperedges

Abstract

Human-centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high-occupancy public transportation, rather than focusing solely on vehicle-centric performance. This paper proposes STDSH-MARL (Spatio-Temporal Dual-Stage Hypergraph based Multi-Agent Reinforcement Learning), a scalable multi-agent deep reinforcement learning framework that follows a centralized training and decentralized execution paradigm. The proposed method captures spatio-temporal dependencies through a novel dual-stage hypergraph attention mechanism that models interactions across both spatial and temporal hyperedges. In addition, a hybrid discrete action space is introduced to jointly determine the next signal phase configuration and its corresponding green duration, enabling more adaptive signal timing decisions. Experiments conducted on a corridor network under five traffic scenarios demonstrate that STDSH-MARL consistently improves multimodal performance and provides clear benefits for public transportation priority. Compared with state-of-the-art baseline methods, the proposed approach achieves superior overall performance. Further ablation studies confirm the contribution of each component of STDSH-MARL, with temporal hyperedges identified as the most influential factor driving the observed performance gains.