{"id":"https://openalex.org/W4308068341","doi":"https://doi.org/10.1109/itsc55140.2022.9921871","title":"An Approach to Improving Regenerative Energy by Using Swarm Intelligence for Urban Rail Transit","display_name":"An Approach to Improving Regenerative Energy by Using Swarm Intelligence for Urban Rail Transit","publication_year":2022,"publication_date":"2022-10-08","ids":{"openalex":"https://openalex.org/W4308068341","doi":"https://doi.org/10.1109/itsc55140.2022.9921871"},"language":"en","primary_location":{"id":"doi:10.1109/itsc55140.2022.9921871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc55140.2022.9921871","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-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/A5100458758","display_name":"Hao Liu","orcid":"https://orcid.org/0000-0001-8744-9588"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Liu","raw_affiliation_strings":["Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059372733","display_name":"Jing Xun","orcid":"https://orcid.org/0000-0003-1656-4719"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Xun","raw_affiliation_strings":["Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023364415","display_name":"Juanjuan Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juanjuan Cai","raw_affiliation_strings":["Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100684710","display_name":"Yafei Liu","orcid":"https://orcid.org/0000-0002-2135-0232"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yafei Liu","raw_affiliation_strings":["Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety,Beijing,China,100044","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078042992","display_name":"Xuguang Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuguang Wen","raw_affiliation_strings":["Nanning University,Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation,Nanning,China,541600"],"affiliations":[{"raw_affiliation_string":"Nanning University,Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation,Nanning,China,541600","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100458758"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.3468,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44797788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"499","last_page":"504"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9998000264167786,"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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T13439","display_name":"Transport and Economic Policies","score":0.9830999970436096,"subfield":{"id":"https://openalex.org/subfields/1408","display_name":"Strategy and Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.727019190788269},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.6819183826446533},{"id":"https://openalex.org/keywords/regenerative-brake","display_name":"Regenerative brake","score":0.6296149492263794},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5242526531219482},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5198469758033752},{"id":"https://openalex.org/keywords/urban-rail-transit","display_name":"Urban rail transit","score":0.5080204010009766},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4783467948436737},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4679279327392578},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.425123393535614},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.38263583183288574},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.37917858362197876},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.30292344093322754},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2317943572998047},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1469806730747223},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.09987276792526245},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.0938226580619812},{"id":"https://openalex.org/keywords/brake","display_name":"Brake","score":0.07622620463371277}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.727019190788269},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.6819183826446533},{"id":"https://openalex.org/C162380856","wikidata":"https://www.wikidata.org/wiki/Q580078","display_name":"Regenerative brake","level":3,"score":0.6296149492263794},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5242526531219482},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5198469758033752},{"id":"https://openalex.org/C2780434240","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Urban rail transit","level":2,"score":0.5080204010009766},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4783467948436737},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4679279327392578},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.425123393535614},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.38263583183288574},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.37917858362197876},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.30292344093322754},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2317943572998047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1469806730747223},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.09987276792526245},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0938226580619812},{"id":"https://openalex.org/C2780999251","wikidata":"https://www.wikidata.org/wiki/Q17022503","display_name":"Brake","level":2,"score":0.07622620463371277},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"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/itsc55140.2022.9921871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc55140.2022.9921871","pdf_url":null,"source":{"id":"https://openalex.org/S4363607737","display_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8700000047683716}],"awards":[{"id":"https://openalex.org/G4195470973","display_name":null,"funder_award_id":"62073026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5871965333","display_name":null,"funder_award_id":"2022JBMC066","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325705","display_name":"Scientific Research and Technology Development Program of Guangxi","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W618248309","https://openalex.org/W2377336366","https://openalex.org/W2049765532","https://openalex.org/W1568097102","https://openalex.org/W1601203902","https://openalex.org/W2024992299","https://openalex.org/W4362495566","https://openalex.org/W3014342922","https://openalex.org/W4229442600","https://openalex.org/W4213342684"],"abstract_inverted_index":{"Urban":[0],"rail":[1],"transit":[2],"(URT)":[3],"develops":[4],"rapidly":[5],"in":[6,86,165,197,213],"modern":[7],"cities,":[8],"and":[9,79,107,133,148],"its":[10],"energy":[11,21,75,83,194],"efficiency":[12],"attracts":[13],"extensive":[14],"attention.":[15],"The":[16,184],"utilization":[17],"of":[18,30,38,59,111,130,162],"regenerative":[19],"braking":[20],"(RBE)":[22],"is":[23,102,123,172],"an":[24],"important":[25],"method":[26,44,171,190],"for":[27,64,114],"energy-efficient":[28],"operation":[29,177],"URT.":[31],"In":[32],"order":[33],"to":[34,45,104,125,157],"make":[35],"full":[36],"use":[37],"RBE,":[39],"this":[40],"paper":[41],"proposed":[42,170,189],"a":[43,98,149],"maximize":[46,105],"RBE":[47,106,138,161,210],"by":[48,135,211],"train":[49,65,69,87,116,120,131,163,199],"group":[50,66,70],"matching":[51,76,84,195],"with":[52,74,202,215],"swarm":[53],"intelligence":[54],"algorithm.":[55],"First,":[56],"four":[57],"cases":[58],"overlapping":[60,128],"time":[61,122,129],"are":[62,89,155],"defined":[63],"matching.":[67],"A":[68,142],"can":[71,139,191],"be":[72,140],"formed":[73],"between":[77],"trains":[78],"the":[80,94,109,119,127,137,159,169,176,180,188,193,203,206],"moments":[81],"when":[82],"occurs":[85],"groups":[88,164],"found":[90],"out":[91],"based":[92,174],"on":[93,175,218],"actual":[95],"timetable.":[96],"Then,":[97],"dual-objective":[99],"optimization":[100],"model":[101],"formulated":[103],"reduce":[108],"deviation":[110],"departure":[112,219],"times":[113],"all":[115],"groups.":[117],"Specifically,":[118],"dwell":[121],"adjusted":[124],"increase":[126],"traction":[132],"braking,":[134],"which":[136],"improved.":[141],"Particle":[143],"Swarm":[144],"Optimization":[145,152],"(PSO)":[146],"algorithm":[147,154],"Gray":[150],"Wolf":[151],"(GWO)":[153],"developed":[156],"solve":[158],"optimal":[160],"different":[166],"scenarios.":[167],"Finally,":[168],"evaluated":[173],"data":[178],"from":[179],"Beijing":[181],"YANFANG":[182],"Line.":[183],"results":[185],"show":[186],"that":[187],"clarify":[192],"situations":[196],"each":[198],"group.":[200],"Compared":[201],"original":[204],"timetable,":[205],"optimized":[207],"timetable":[208],"improved":[209],"38.3%":[212],"average":[214],"acceptable":[216],"influence":[217],"times.":[220]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}