{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T03:11:39Z","timestamp":1776309099867,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Recent advancements in Deep Reinforcement Learning (DRL) and Graph Neural Network (GNN) have demonstrated notable promise in the realm of intelligent traffic signal control, facilitating the coordination across multiple intersections. However, the traditional methods rely on standard graph structures often fail to capture the intricate higher-order spatio-temporal correlations inherent in real-world traffic dynamics. Standard graphs cannot fully represent the spatial relationships within road networks, which limits the effectiveness of graph-based approaches. In contrast, directed hypergraphs provide more accurate representation of spatial information to model complex directed relationships among multiple nodes. In this paper, we propose DHLight, a novel multi-agent policy-based framework that synergistically integrates directed hypergraph learning module. This framework introduces a novel dynamic directed hypergraph construction mechanism, which captures complex and evolving spatio-temporal relationships among intersections in road networks. By leveraging the directed hypergraph relational structure, DHLight empowers agents to achieve adaptive decision-making in traffic signal control. The effectiveness of DHLight is validated against state-of-the-art baselines through extensive experiments in various network datasets. We release the code to support the reproducibility of this work at https:\/\/github.com\/LuckyVoasem\/Traffic-Light-control<\/jats:p>","DOI":"10.3233\/faia251179","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:49Z","timestamp":1761126829000},"source":"Crossref","is-referenced-by-count":1,"title":["DHLight: Multi-Agent Policy-Based Directed Hypergraph Learning for Traffic Signal Control"],"prefix":"10.3233","author":[{"given":"Zhen","family":"Lei","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhishu","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Nottingham Ningbo China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiehua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tongji University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251179","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:53:50Z","timestamp":1761126830000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251179","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}