{"id":"https://openalex.org/W4415428223","doi":"https://doi.org/10.3233/faia251179","title":"DHLight: Multi-Agent Policy-Based Directed Hypergraph Learning for Traffic Signal Control","display_name":"DHLight: Multi-Agent Policy-Based Directed Hypergraph Learning for Traffic Signal Control","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415428223","doi":"https://doi.org/10.3233/faia251179"},"language":null,"primary_location":{"id":"doi:10.3233/faia251179","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251179","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251179","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109299788","display_name":"Zhen Lei","orcid":"https://orcid.org/0000-0002-0791-189X"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhen Lei","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019778525","display_name":"Zhishu Shen","orcid":"https://orcid.org/0000-0002-3123-4390"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhishu Shen","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381632","display_name":"Kang Wang","orcid":"https://orcid.org/0000-0001-5401-1803"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Wang","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100732682","display_name":"Zhenwei Wang","orcid":"https://orcid.org/0000-0001-7524-135X"},"institutions":[{"id":"https://openalex.org/I13591777","display_name":"University of Nottingham Ningbo China","ror":"https://ror.org/03y4dt428","country_code":"CN","type":"education","lineage":["https://openalex.org/I13591777","https://openalex.org/I142263535"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenwei Wang","raw_affiliation_strings":["School of Computer Science, University of Nottingham Ningbo China, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Nottingham Ningbo China, China","institution_ids":["https://openalex.org/I13591777"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020793859","display_name":"Tiehua Zhang","orcid":"https://orcid.org/0000-0002-7195-4472"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiehua Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Tongji University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Tongji University, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.9056,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95084883,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9702000021934509,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9702000021934509,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10603","display_name":"Smart Grid Energy Management","score":0.9009000062942505,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.7652000188827515},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5620999932289124},{"id":"https://openalex.org/keywords/directed-graph","display_name":"Directed graph","score":0.538100004196167},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.507099986076355},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44749999046325684},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3776000142097473},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3513999879360199}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.7652000188827515},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7164999842643738},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5620999932289124},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.538100004196167},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.507099986076355},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5005999803543091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44920000433921814},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44749999046325684},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3776000142097473},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3662000000476837},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3255000114440918},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3215999901294708},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2775999903678894},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.25}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia251179","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251179","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia251179","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251179","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,16,49,127,144,160],"Deep":[3],"Reinforcement":[4],"Learning":[5],"(DRL)":[6],"and":[7,121],"Graph":[8],"Neural":[9],"Network":[10],"(GNN)":[11],"have":[12],"demonstrated":[13],"notable":[14],"promise":[15],"the":[17,25,31,43,58,66,132,166,170],"realm":[18],"of":[19,68,79,150,172],"intelligent":[20],"traffic":[21,51,145],"signal":[22,146],"control,":[23],"facilitating":[24],"coordination":[26],"across":[27],"multiple":[28,88],"intersections.":[29],"However,":[30],"traditional":[32],"methods":[33],"rely":[34],"on":[35],"standard":[36],"graph":[37],"structures":[38],"often":[39],"fail":[40],"to":[41,82,140,168],"capture":[42],"intricate":[44],"higher-order":[45],"spatio-temporal":[46,123],"correlations":[47],"inherent":[48],"real-world":[50],"dynamics.":[52],"Standard":[53],"graphs":[54],"cannot":[55],"fully":[56],"represent":[57],"spatial":[59,80],"relationships":[60,86,124],"within":[61],"road":[62,128],"networks,":[63],"which":[64,118],"limits":[65],"effectiveness":[67,149],"graph-based":[69],"approaches.":[70],"In":[71,90],"contrast,":[72],"directed":[73,85,104,114,133],"hypergraphs":[74],"provide":[75],"more":[76],"accurate":[77],"representation":[78],"information":[81],"model":[83],"complex":[84,120],"among":[87,125],"nodes.":[89],"this":[91,173],"paper,":[92],"we":[93],"propose":[94],"DHLight,":[95],"a":[96,111],"novel":[97,112],"multi-agent":[98],"policy-based":[99],"framework":[100,109],"that":[101],"synergistically":[102],"integrates":[103],"hypergraph":[105,115,134],"learning":[106],"module.":[107],"This":[108],"introduces":[110],"dynamic":[113],"construction":[116],"mechanism,":[117],"captures":[119],"evolving":[122],"intersections":[126],"networks.":[129],"By":[130],"leveraging":[131],"relational":[135],"structure,":[136],"DHLight":[137,151],"empowers":[138],"agents":[139],"achieve":[141],"adaptive":[142],"decision-making":[143],"control.":[147],"The":[148],"is":[152],"validated":[153],"against":[154],"state-of-the-art":[155],"baselines":[156],"through":[157],"extensive":[158],"experiments":[159],"various":[161],"network":[162],"datasets.":[163],"We":[164],"release":[165],"code":[167],"support":[169],"reproducibility":[171],"work":[174],"at":[175],"https://github.com/LuckyVoasem/Traffic-Light-control":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-24T00:00:00"}