{"id":"https://openalex.org/W4406457494","doi":"https://doi.org/10.1109/tits.2025.3525538","title":"Soft Actor-Critic Deep Reinforcement Learning for Train Timetable Collaborative Optimization of Large-Scale Urban Rail Transit Network Under Dynamic Demand","display_name":"Soft Actor-Critic Deep Reinforcement Learning for Train Timetable Collaborative Optimization of Large-Scale Urban Rail Transit Network Under Dynamic Demand","publication_year":2025,"publication_date":"2025-01-16","ids":{"openalex":"https://openalex.org/W4406457494","doi":"https://doi.org/10.1109/tits.2025.3525538"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3525538","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3525538","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/A5109735032","display_name":"Longhui Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Longhui Wen","raw_affiliation_strings":["Intelligent Transportation System Research Center, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0008-2621-6578","affiliations":[{"raw_affiliation_string":"Intelligent Transportation System Research Center, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023397764","display_name":"Liyang Hu","orcid":"https://orcid.org/0000-0003-4631-6062"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liyang Hu","raw_affiliation_strings":["Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China","Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-4631-6062","affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000080881","display_name":"Wei Zhou","orcid":"https://orcid.org/0000-0003-3225-0576"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhou","raw_affiliation_strings":["Intelligent Transportation System Research Center, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-3225-0576","affiliations":[{"raw_affiliation_string":"Intelligent Transportation System Research Center, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004466915","display_name":"Gang Ren","orcid":"https://orcid.org/0000-0002-3412-0831"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Ren","raw_affiliation_strings":["Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China","Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-3412-0831","affiliations":[{"raw_affiliation_string":"Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]},{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100404928","display_name":"Ning Zhang","orcid":"https://orcid.org/0000-0003-4721-1609"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Zhang","raw_affiliation_strings":["Intelligent Transportation System Research Center, Southeast University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Transportation System Research Center, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":13.6161,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.988439,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"26","issue":"5","first_page":"7021","last_page":"7035"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9757999777793884,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9757999777793884,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9726999998092651,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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":0.9287999868392944,"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":0.7455399036407471},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5468551516532898},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.5319854021072388},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5060285925865173},{"id":"https://openalex.org/keywords/public-transport","display_name":"Public transport","score":0.45985299348831177},{"id":"https://openalex.org/keywords/transit","display_name":"Transit (satellite)","score":0.442573219537735},{"id":"https://openalex.org/keywords/urban-rail-transit","display_name":"Urban rail transit","score":0.43255162239074707},{"id":"https://openalex.org/keywords/rail-transit","display_name":"Rail transit","score":0.42175114154815674},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3966625928878784},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3623793125152588},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3444884419441223},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33542951941490173},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10662707686424255}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7455399036407471},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5468551516532898},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.5319854021072388},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5060285925865173},{"id":"https://openalex.org/C539828613","wikidata":"https://www.wikidata.org/wiki/Q178512","display_name":"Public transport","level":2,"score":0.45985299348831177},{"id":"https://openalex.org/C2778022998","wikidata":"https://www.wikidata.org/wiki/Q651136","display_name":"Transit (satellite)","level":3,"score":0.442573219537735},{"id":"https://openalex.org/C2780434240","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Urban rail transit","level":2,"score":0.43255162239074707},{"id":"https://openalex.org/C2992701429","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Rail transit","level":2,"score":0.42175114154815674},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3966625928878784},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3623793125152588},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3444884419441223},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33542951941490173},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10662707686424255},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3525538","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3525538","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":[{"id":"https://metadata.un.org/sdg/11","score":0.6700000166893005,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G7398469497","display_name":null,"funder_award_id":"52372314","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W2009767457","https://openalex.org/W2014805780","https://openalex.org/W2038607142","https://openalex.org/W2062621951","https://openalex.org/W2068341557","https://openalex.org/W2077613353","https://openalex.org/W2177295793","https://openalex.org/W2337617318","https://openalex.org/W2345655518","https://openalex.org/W2426501523","https://openalex.org/W2745485547","https://openalex.org/W2746553466","https://openalex.org/W2792946985","https://openalex.org/W2793811993","https://openalex.org/W2800460440","https://openalex.org/W2800510013","https://openalex.org/W2901467911","https://openalex.org/W2904246096","https://openalex.org/W2914475616","https://openalex.org/W3000701615","https://openalex.org/W3006746595","https://openalex.org/W3010786073","https://openalex.org/W3084058509","https://openalex.org/W3118721436","https://openalex.org/W3135864150","https://openalex.org/W3136740735","https://openalex.org/W3186135363","https://openalex.org/W3210398659","https://openalex.org/W3216409334","https://openalex.org/W4200110405","https://openalex.org/W4200115322","https://openalex.org/W4214496626","https://openalex.org/W4214717370","https://openalex.org/W4226532250","https://openalex.org/W4229007983","https://openalex.org/W4243500977","https://openalex.org/W4280594981","https://openalex.org/W4300167416","https://openalex.org/W4311396602","https://openalex.org/W4312742848","https://openalex.org/W4323572274","https://openalex.org/W4362650413","https://openalex.org/W4375858308","https://openalex.org/W4385913113","https://openalex.org/W4387123548","https://openalex.org/W4387385706","https://openalex.org/W4387609345","https://openalex.org/W4399802333","https://openalex.org/W6637967152","https://openalex.org/W6683195989","https://openalex.org/W6684205842","https://openalex.org/W6684921986","https://openalex.org/W6685444567","https://openalex.org/W6747473740","https://openalex.org/W6748839928","https://openalex.org/W6775522024","https://openalex.org/W6796289742"],"related_works":["https://openalex.org/W3048859969","https://openalex.org/W2052743154","https://openalex.org/W3162329824","https://openalex.org/W2011781613","https://openalex.org/W2953473307","https://openalex.org/W1980504576","https://openalex.org/W4388420020","https://openalex.org/W4238517002","https://openalex.org/W4388138713","https://openalex.org/W1520175929"],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,23,58,102,106,112,138,155,158,202],"collaborative":[3],"issue":[4],"in":[5,182],"large-scale":[6,148],"urban":[7],"rail":[8],"transit":[9],"(URT)":[10],"network":[11,150],"operations,":[12],"this":[13],"paper":[14],"proposes":[15],"an":[16,70],"adaptive":[17],"real-time":[18,103],"control":[19,59],"framework":[20,140],"based":[21,136],"on":[22,125,137,146],"Soft":[24],"Actor-Critic":[25],"(SAC)":[26],"deep":[27],"reinforcement":[28,167],"learning":[29,168],"(DRL)":[30],"method,":[31],"featuring":[32],"flexible":[33],"train":[34,49,82,88,122],"scheduling":[35,89],"capabilities.":[36],"First,":[37],"by":[38,111],"analyzing":[39],"dynamic":[40],"passenger":[41,96,184],"travel":[42],"behavior":[43],"(e.g.,":[44,52],"entering/exiting":[45],"stations,":[46],"transferring)":[47],"and":[48,69,84,98,121,170],"operation":[50],"events":[51],"dispatching,":[53],"interstation":[54],"running,":[55],"station":[56],"dwelling),":[57],"problem":[60],"is":[61,75,91,109,141],"modeled":[62],"as":[63,81,130],"a":[64,87,147,179,197],"Markov":[65],"Decision":[66],"Process":[67],"(MDP)":[68],"efficient":[71],"URT":[72,107,149],"simulation":[73],"environment":[74],"constructed.":[76],"Then,":[77],"considering":[78],"constraints":[79],"such":[80],"capacity":[83],"dispatch":[85,123],"intervals,":[86],"model":[90],"developed":[92],"to":[93,165,188,201],"minimize":[94],"both":[95],"costs":[97],"operational":[99],"costs.":[100],"Subsequently,":[101],"state":[104],"of":[105,115,157],"system":[108],"represented":[110],"overall":[113],"number":[114],"passengers":[116],"present":[117],"at":[118],"every":[119],"platform,":[120],"intervals":[124],"all":[126],"lines":[127,153],"are":[128],"used":[129],"decision":[131],"variables.":[132],"A":[133],"solving":[134],"algorithm":[135],"SAC":[139],"developed.":[142],"Finally,":[143],"experimental":[144],"results":[145],"comprising":[151],"10":[152],"demonstrate":[154],"effectiveness":[156],"proposed":[159,176],"framework,":[160],"showing":[161],"superior":[162],"performance":[163],"compared":[164,200],"other":[166],"algorithms":[169],"traditional":[171],"heuristic":[172],"optimization":[173],"algorithms.":[174],"The":[175],"approach":[177],"achieves":[178],"1.63%":[180],"reduction":[181],"average":[183],"waiting":[185],"time,":[186],"equivalent":[187],"2.09":[189],"seconds,":[190],"while":[191],"utilizing":[192],"49":[193],"fewer":[194],"trains,":[195],"representing":[196],"2.97%":[198],"decrease,":[199],"second-best":[203],"TD3":[204],"algorithm.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
