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

arXiv: https://arxiv.org/abs/2602.17068 · HTML: https://mlsanity.com/paper/2602.17068/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023)
- [Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1908.03761/) (2019)
- [Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning](https://mlsanity.com/paper/2104.09936/) (2021)
- [EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System](https://mlsanity.com/paper/2206.13441/) (2022)
- [Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control](https://mlsanity.com/paper/2310.02435/) (2023)
- [Reinforcement learning for traffic signal control in hybrid action space](https://mlsanity.com/paper/2211.12956/) (2022)
- [Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs](https://mlsanity.com/paper/2404.11014/) (2024)
- [Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control](https://mlsanity.com/paper/2203.04310/) (2022)
- [A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control](https://mlsanity.com/paper/2503.23626/) (2025)
- [Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation](https://mlsanity.com/paper/2601.10911/) (2026)
- [Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2602.02959/) (2026)

## Abstract (arXiv)

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 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 achieves strong overall multimodal performance, with substantial and relatively consistent reductions in tram waiting time, while improvements in bus waiting time are more variable across traffic scenarios. These results highlight the trade-off among overall network efficiency, tram priority, and bus service quality. 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.