{"id":"https://openalex.org/W4226518300","doi":"https://doi.org/10.1109/jiot.2022.3167029","title":"Context-Aware Multiagent Broad Reinforcement Learning for Mixed Pedestrian-Vehicle Adaptive Traffic Light Control","display_name":"Context-Aware Multiagent Broad Reinforcement Learning for Mixed Pedestrian-Vehicle Adaptive Traffic Light Control","publication_year":2022,"publication_date":"2022-04-13","ids":{"openalex":"https://openalex.org/W4226518300","doi":"https://doi.org/10.1109/jiot.2022.3167029"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2022.3167029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2022.3167029","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027563148","display_name":"Ruijie Zhu","orcid":"https://orcid.org/0000-0003-1210-546X"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruijie Zhu","raw_affiliation_strings":["School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032184869","display_name":"Shuning Wu","orcid":"https://orcid.org/0000-0003-1684-8279"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuning Wu","raw_affiliation_strings":["School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100421909","display_name":"Lulu Li","orcid":"https://orcid.org/0000-0003-1359-340X"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lulu Li","raw_affiliation_strings":["School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055787676","display_name":"Ping Lv","orcid":null},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Lv","raw_affiliation_strings":["School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081346568","display_name":"Mingliang Xu","orcid":"https://orcid.org/0000-0002-6885-3451"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingliang Xu","raw_affiliation_strings":["School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5027563148"],"corresponding_institution_ids":["https://openalex.org/I38877650"],"apc_list":null,"apc_paid":null,"fwci":6.0765,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.97087211,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"9","issue":"20","first_page":"19694","last_page":"19705"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10524","display_name":"Traffic control and management","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.993399977684021,"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/T12546","display_name":"Smart Parking Systems Research","score":0.9925000071525574,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.9356663823127747},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7880970239639282},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6223235130310059},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.5954456925392151},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4937163293361664},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4463573098182678},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.44018441438674927},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.4184980094432831},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33782321214675903},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1133095920085907},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.09341442584991455}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.9356663823127747},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7880970239639282},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6223235130310059},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.5954456925392151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4937163293361664},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4463573098182678},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.44018441438674927},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.4184980094432831},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33782321214675903},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1133095920085907},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.09341442584991455},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2022.3167029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2022.3167029","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5699999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G1381433840","display_name":null,"funder_award_id":"2021T140622","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7823778866","display_name":null,"funder_award_id":"62001422","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1998460924","https://openalex.org/W2017633777","https://openalex.org/W2068849277","https://openalex.org/W2145339207","https://openalex.org/W2553067200","https://openalex.org/W2618843071","https://openalex.org/W2738226240","https://openalex.org/W2746553466","https://openalex.org/W2784233306","https://openalex.org/W2794842204","https://openalex.org/W2803155336","https://openalex.org/W2890126432","https://openalex.org/W2895874728","https://openalex.org/W2898035736","https://openalex.org/W2915117209","https://openalex.org/W2932176981","https://openalex.org/W2933570795","https://openalex.org/W2951974123","https://openalex.org/W2965237761","https://openalex.org/W2969262963","https://openalex.org/W2983699029","https://openalex.org/W3002641953","https://openalex.org/W3011978531","https://openalex.org/W3013696755","https://openalex.org/W3022124280","https://openalex.org/W3030840723","https://openalex.org/W3035285280","https://openalex.org/W3043140559","https://openalex.org/W3083917758","https://openalex.org/W3091266930","https://openalex.org/W3096739060","https://openalex.org/W3106357768","https://openalex.org/W3108226879","https://openalex.org/W3108669277","https://openalex.org/W3110101394","https://openalex.org/W3112007002","https://openalex.org/W3112545933","https://openalex.org/W3115913152","https://openalex.org/W3115989973","https://openalex.org/W3121412752","https://openalex.org/W3122010492","https://openalex.org/W3128049868","https://openalex.org/W3128347067","https://openalex.org/W3134302667","https://openalex.org/W3136849308","https://openalex.org/W3157947470","https://openalex.org/W3175224103","https://openalex.org/W3202928841","https://openalex.org/W3205137522","https://openalex.org/W4299802797","https://openalex.org/W6628769285","https://openalex.org/W6684205842","https://openalex.org/W6685444567","https://openalex.org/W6687681856","https://openalex.org/W6738796088","https://openalex.org/W6751139674"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2973192971","https://openalex.org/W4390341805","https://openalex.org/W3069032","https://openalex.org/W4210448965","https://openalex.org/W4360619413"],"abstract_inverted_index":{"Efficient":[0],"traffic":[1,38,65,144],"light":[2,66],"control":[3,37,67],"is":[4,152],"a":[5,46,78,87],"critical":[6],"part":[7],"of":[8,86,98,105,122],"realizing":[9],"smart":[10],"transportation.":[11],"In":[12,41],"particular,":[13],"deep":[14,21,88],"reinforcement":[15,51,58,158],"learning":[16,52,59,73,159],"(DRL)":[17],"algorithms":[18],"that":[19,94,118,150],"use":[20],"neural":[22],"networks":[23],"(DNNs)":[24],"have":[25],"superior":[26,153],"autonomous":[27],"decision-making":[28],"ability.":[29],"Most":[30],"existing":[31],"work":[32],"has":[33],"applied":[34],"DRL":[35],"to":[36,82,142,154],"lights":[39],"intelligently.":[40],"this":[42],"article,":[43],"we":[44],"propose":[45],"novel":[47],"context-aware":[48,116],"multiagent":[49,157],"broad":[50,57,72],"(CAMABRL)":[53],"approach":[54],"based":[55],"on":[56],"(BRL)":[60],"for":[61],"mixed":[62],"pedestrian-vehicle":[63],"adaptive":[64],"(ATLC).":[68],"CAMABRL":[69,100,151],"exploits":[70],"the":[71,96,103,109,115,120,131],"system":[74],"(BLS)":[75],"established":[76],"in":[77],"flat":[79],"network":[80,89],"structure":[81],"make":[83,139],"decisions":[84,141],"instead":[85],"structure.":[90],"Unlike":[91],"previous":[92],"works":[93],"consider":[95],"attributes":[97],"vehicles,":[99],"also":[101],"takes":[102],"states":[104,121],"pedestrians":[106],"waiting":[107],"at":[108],"intersection":[110],"into":[111],"consideration.":[112],"Combining":[113],"with":[114],"mechanism":[117],"utilizes":[119],"adjacent":[123],"agents":[124,137],"and":[125],"potential":[126],"state":[127],"information":[128],"captured":[129],"by":[130],"long":[132],"short-term":[133],"memory":[134],"(LSTM)":[135],"network,":[136],"can":[138],"farsighted":[140],"alleviate":[143],"congestion.":[145],"The":[146],"experimental":[147],"results":[148],"show":[149],"several":[155],"state-of-the-art":[156],"(MARL)":[160],"methods.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":3}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-10T00:00:00"}
