{"id":"https://openalex.org/W4416177831","doi":"https://doi.org/10.1109/tits.2025.3629415","title":"ETLight: An Evolution Transformer for Efficient Traffic Signal Control","display_name":"ETLight: An Evolution Transformer for Efficient Traffic Signal Control","publication_year":2025,"publication_date":"2025-11-13","ids":{"openalex":"https://openalex.org/W4416177831","doi":"https://doi.org/10.1109/tits.2025.3629415"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3629415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3629415","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","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/A5101454723","display_name":"Hui Jiang","orcid":"https://orcid.org/0000-0003-4062-7206"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hui Jiang","raw_affiliation_strings":["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, the National Engineering Research Center for Visual Information and Applications, and the Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, the National Engineering Research Center for Visual Information and Applications, and the Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033808402","display_name":"Meiqin Liu","orcid":"https://orcid.org/0000-0003-0693-6574"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Meiqin Liu","raw_affiliation_strings":["College of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0693-6574","affiliations":[{"raw_affiliation_string":"College of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003643230","display_name":"Senlin Zhang","orcid":"https://orcid.org/0000-0001-5117-3110"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senlin Zhang","raw_affiliation_strings":["College of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5117-3110","affiliations":[{"raw_affiliation_string":"College of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048107920","display_name":"Ronghao Zheng","orcid":"https://orcid.org/0000-0002-9095-5905"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ronghao Zheng","raw_affiliation_strings":["College of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9095-5905","affiliations":[{"raw_affiliation_string":"College of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004998505","display_name":"Shanling Dong","orcid":"https://orcid.org/0000-0002-1754-1829"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shanling Dong","raw_affiliation_strings":["College of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1754-1829","affiliations":[{"raw_affiliation_string":"College of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006277484","display_name":"Xuguang Lan","orcid":"https://orcid.org/0000-0002-3422-944X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuguang Lan","raw_affiliation_strings":["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, the National Engineering Research Center for Visual Information and Applications, and the Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, the National Engineering Research Center for Visual Information and Applications, and the Institute of Artificial Intelligence and Robotics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101454723"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40850795,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":"1","first_page":"1328","last_page":"1337"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.8343999981880188,"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":0.8343999981880188,"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":0.094200000166893,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.004800000227987766,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6668999791145325},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6366999745368958},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5461000204086304},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.520799994468689},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4487999975681305},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.3573000133037567},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3538999855518341},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.3382999897003174}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6668999791145325},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6366999745368958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6051999926567078},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5461000204086304},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.520799994468689},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4410000145435333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4214000105857849},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3573000133037567},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3262999951839447},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32499998807907104},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.303600013256073},{"id":"https://openalex.org/C155386361","wikidata":"https://www.wikidata.org/wiki/Q1649571","display_name":"Process control","level":3,"score":0.3027999997138977},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.28999999165534973},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C117619785","wikidata":"https://www.wikidata.org/wiki/Q6094414","display_name":"Iterative learning control","level":3,"score":0.273499995470047},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C149271511","wikidata":"https://www.wikidata.org/wiki/Q1417149","display_name":"Rule-based system","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.25949999690055847},{"id":"https://openalex.org/C2779127903","wikidata":"https://www.wikidata.org/wiki/Q6510194","display_name":"Learning rule","level":3,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3629415","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3629415","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6064383831","display_name":null,"funder_award_id":"2021ZD0112703","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1516835682","https://openalex.org/W2007894170","https://openalex.org/W2031530760","https://openalex.org/W2122241715","https://openalex.org/W2145339207","https://openalex.org/W2162578038","https://openalex.org/W2194775991","https://openalex.org/W2754517384","https://openalex.org/W2886958893","https://openalex.org/W2915117209","https://openalex.org/W2945991855","https://openalex.org/W2964749398","https://openalex.org/W2983178256","https://openalex.org/W2988973041","https://openalex.org/W2998187693","https://openalex.org/W4308673398","https://openalex.org/W4323262648","https://openalex.org/W4385245566","https://openalex.org/W4385568146","https://openalex.org/W4386806382","https://openalex.org/W4388320480","https://openalex.org/W4390044083","https://openalex.org/W4390492454","https://openalex.org/W4391917818","https://openalex.org/W4394643672","https://openalex.org/W4401863490","https://openalex.org/W4404739549","https://openalex.org/W4409149645"],"related_works":[],"abstract_inverted_index":{"Traffic":[0],"signal":[1],"control":[2],"(TSC)":[3],"is":[4,46,83,230],"still":[5],"one":[6],"of":[7,18,43,77,91,144,186],"the":[8,16,41,75,79,89,95,114,124,130,138,149,174,194],"most":[9],"challenging":[10],"and":[11,54,87,105,121,154,191,198,213,229],"promising":[12],"research":[13],"issues":[14],"in":[15,25,66,123],"field":[17,76],"transportation.":[19],"Since":[20],"traditional":[21],"methods":[22,34,220],"have":[23,35],"difficulty":[24],"handling":[26],"dynamically":[27],"changing":[28],"traffic":[29,125],"flows,":[30],"reinforcement":[31],"learning":[32,56,150,208,211],"(RL)":[33],"been":[36],"introduced":[37],"into":[38,100],"TSC.":[39],"However,":[40],"cost":[42],"practical":[44],"application":[45],"critically":[47],"high":[48],"due":[49],"to":[50,74,85,112,127,147,188,217,233],"multiple":[51],"sampling":[52],"trials":[53],"long":[55],"process.":[57],"The":[58],"Transformer":[59,81,110,199],"architecture":[60,82,111],"has":[61,204,226],"recently":[62],"attained":[63],"remarkable":[64],"results":[65],"natural":[67],"language":[68],"processing":[69],"(NLP),":[70],"but":[71],"when":[72],"applied":[73],"RL,":[78],"standard":[80],"difficult":[84],"optimize":[86],"faces":[88],"problem":[90],"hyperparameter":[92,234],"sensitivity.":[93],"In":[94,134],"paper,":[96],"we":[97,136,161],"transform":[98],"TSC":[99,219],"a":[101,107,168,205],"sequence":[102],"modeling":[103],"issue":[104],"propose":[106],"new":[108],"evolution":[109,140],"adjust":[113],"autoregressive":[115],"model":[116,166],"through":[117],"reward,":[118],"past":[119],"states":[120],"actions":[122],"environment":[126],"directly":[128],"generate":[129],"best":[131,176,195],"predicted":[132],"action.":[133],"addition,":[135],"use":[137],"feature":[139],"module":[141],"(FEM)":[142],"instead":[143],"residual":[145],"connections":[146],"make":[148],"process":[151],"more":[152,206],"stable":[153,207],"efficient.":[155],"Through":[156],"experiments":[157],"on":[158,178],"public":[159],"datasets,":[160],"demonstrate":[162],"that":[163],"our":[164],"ETLight":[165],"achieves":[167,173],"state-of-the-art":[169],"(SOTA):":[170],"1)":[171],"It":[172,203],"overall":[175],"performance":[177],"average":[179],"travel":[180],"time":[181],"(ATT)":[182],"metric,":[183],"with":[184],"improvements":[185],"up":[187],"6.85%,":[189],"3.73%":[190],"3.10%":[192],"over":[193],"conventional,":[196],"RL":[197],"methods,":[200],"respectively;":[201],"2)":[202],"process,":[209],"faster":[210],"speed":[212],"better":[214],"convergence":[215],"compared":[216],"published":[218],"so":[221],"far;":[222],"and;":[223],"3)":[224],"it":[225],"good":[227],"robustness":[228],"less":[231],"sensitive":[232],"selection.":[235]},"counts_by_year":[],"updated_date":"2026-01-02T23:11:23.791532","created_date":"2025-11-13T00:00:00"}
