{"id":"https://openalex.org/W4409257752","doi":"https://doi.org/10.1109/tits.2025.3554799","title":"Dynamic Optimization of Transportation Networks Using Big Data-Driven Reinforcement Learning","display_name":"Dynamic Optimization of Transportation Networks Using Big Data-Driven Reinforcement Learning","publication_year":2025,"publication_date":"2025-04-08","ids":{"openalex":"https://openalex.org/W4409257752","doi":"https://doi.org/10.1109/tits.2025.3554799"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3554799","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3554799","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/A5113361346","display_name":"Xin Wang","orcid":"https://orcid.org/0009-0004-1235-9296"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004251248","display_name":"Shalli Rani","orcid":"https://orcid.org/0000-0002-8474-9435"},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shalli Rani","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India"],"raw_orcid":"https://orcid.org/0000-0002-8474-9435","affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103208098","display_name":"Xia Cao","orcid":"https://orcid.org/0000-0001-9228-276X"},"institutions":[{"id":"https://openalex.org/I91656880","display_name":"China Medical University","ror":"https://ror.org/032d4f246","country_code":"CN","type":"education","lineage":["https://openalex.org/I91656880"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Cao","raw_affiliation_strings":["Department of Data Center, Shengjing Hospital, China Medical University, Shenyang, China"],"raw_orcid":"https://orcid.org/0000-0001-9228-276X","affiliations":[{"raw_affiliation_string":"Department of Data Center, Shengjing Hospital, China Medical University, Shenyang, China","institution_ids":["https://openalex.org/I91656880"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3336,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.87552836,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"27","issue":"2","first_page":"2595","last_page":"2606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14420","display_name":"Advanced Research in Systems and Signal Processing","score":0.729200005531311,"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/T14420","display_name":"Advanced Research in Systems and Signal Processing","score":0.729200005531311,"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":0.699999988079071,"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":0.817436158657074},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5816338062286377},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5001604557037354},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4236612915992737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3938364088535309},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.263070285320282},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.22385439276695251},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1462632715702057}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.817436158657074},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5816338062286377},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5001604557037354},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4236612915992737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3938364088535309},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.263070285320282},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.22385439276695251},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1462632715702057}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3554799","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3554799","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W3009806917","https://openalex.org/W3015302520","https://openalex.org/W3200072650","https://openalex.org/W4225824600","https://openalex.org/W4281634296","https://openalex.org/W4281743228","https://openalex.org/W4285224986","https://openalex.org/W4285279901","https://openalex.org/W4286206354","https://openalex.org/W4286206377","https://openalex.org/W4290647464","https://openalex.org/W4312781300","https://openalex.org/W4317384366","https://openalex.org/W4319342019","https://openalex.org/W4365815591","https://openalex.org/W4366439801","https://openalex.org/W4377971357","https://openalex.org/W4378083305","https://openalex.org/W4383112908","https://openalex.org/W4385061177","https://openalex.org/W4386692436","https://openalex.org/W4387541904","https://openalex.org/W4387782664","https://openalex.org/W4388469846","https://openalex.org/W4388878626","https://openalex.org/W4389342438","https://openalex.org/W4389543839","https://openalex.org/W4390187287","https://openalex.org/W4391430222","https://openalex.org/W4392449656","https://openalex.org/W4396741220","https://openalex.org/W4398190907","https://openalex.org/W4400650031","https://openalex.org/W4400905971","https://openalex.org/W4401354861"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4390608645","https://openalex.org/W4405901645","https://openalex.org/W4394895745","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351"],"abstract_inverted_index":{"The":[0,102],"dynamic":[1,65],"optimization":[2,66,93],"of":[3,34,67,192,206],"large-scale":[4],"transportation":[5,68,209],"networks":[6],"presents":[7],"significant":[8],"challenges":[9,72],"due":[10],"to":[11,29,44,47,113,135,149,152,172,177,198],"their":[12],"complexity,":[13],"stochasticity,":[14],"and":[15,79,109,117,160,204],"the":[16,32,91,153,167,190,201],"need":[17],"for":[18,64],"real-time":[19],"decision-making.":[20],"In":[21],"conventional":[22],"methods,":[23],"there":[24],"is":[25,133],"often":[26],"a":[27,55,85,125],"failure":[28],"employ":[30],"fully":[31],"resources":[33],"big":[35,58,193],"data":[36,81],"present":[37],"in":[38,49,119,183],"cities,":[39],"thus":[40],"not":[41],"being":[42],"able":[43],"respond":[45],"appropriately":[46],"fluctuations":[48],"traffic":[50,158,173],"conditions.":[51],"This":[52],"paper":[53],"introduces":[54],"novel":[56],"enhanced":[57,170],"data-driven":[59,194],"reinforcement":[60,195],"learning":[61,77,115,196],"(EBD-RL)":[62],"algorithm":[63,104,168],"networks,":[69],"addressing":[70],"these":[71],"by":[73,147],"leveraging":[74],"advanced":[75],"machine":[76],"techniques":[78],"heterogeneous":[80],"sources.":[82],"We":[83],"propose":[84],"hierarchical":[86],"control":[87],"framework":[88],"that":[89,130,141],"decomposes":[90],"global":[92],"problem":[94],"into":[95],"manageable":[96],"sub-problems":[97],"while":[98],"maintaining":[99],"network-wide":[100],"coordination.":[101],"EBD-RL":[103,142],"incorporates":[105],"prioritized":[106],"experience":[107],"replay":[108],"adaptive":[110],"exploration":[111],"strategies":[112],"improve":[114,200],"efficiency":[116],"stability":[118],"high-dimensional":[120],"state":[121],"spaces.":[122],"Experiments":[123],"on":[124],"realistic":[126],"urban":[127,208],"network":[128,180],"show":[129,140],"our":[131],"method":[132],"superior":[134],"six":[136],"state-of-the-art":[137],"methods.":[138],"Results":[139],"reduces":[143],"total":[144],"travel":[145],"time":[146,182],"up":[148,176],"30%":[150],"compared":[151],"best":[154],"baseline":[155],"under":[156],"various":[157],"demands":[159],"connected":[161],"autonomous":[162],"vehicle":[163],"penetration":[164],"rates.":[165],"Furthermore,":[166],"exhibits":[169],"resilience":[171,205],"incidents,":[174],"achieving":[175],"33%":[178],"faster":[179],"recovery":[181],"severe":[184],"disruption":[185],"scenarios.":[186],"These":[187],"findings":[188],"highlight":[189],"potential":[191],"approaches":[197],"significantly":[199],"efficiency,":[202],"adaptability,":[203],"modern":[207],"systems.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
