{"id":"https://openalex.org/W4318604495","doi":"https://doi.org/10.1109/ssci51031.2022.10022145","title":"High-speed Train Timetabling Based on Reinforcement Learning","display_name":"High-speed Train Timetabling Based on Reinforcement Learning","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4318604495","doi":"https://doi.org/10.1109/ssci51031.2022.10022145"},"language":"en","primary_location":{"id":"doi:10.1109/ssci51031.2022.10022145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci51031.2022.10022145","pdf_url":null,"source":{"id":"https://openalex.org/S4363605327","display_name":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","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 Symposium Series on Computational Intelligence (SSCI)","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/A5061195572","display_name":"Wanlu Yang","orcid":"https://orcid.org/0000-0003-1571-3241"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanlu Yang","raw_affiliation_strings":["Tsinghua University,Department of Automation and BNRist,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation and BNRist,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046558325","display_name":"Peng Jiang","orcid":"https://orcid.org/0000-0003-4058-009X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Jiang","raw_affiliation_strings":["Tsinghua University,Department of Automation and BNRist,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation and BNRist,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068624597","display_name":"Shiji Song","orcid":"https://orcid.org/0000-0001-7361-9283"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiji Song","raw_affiliation_strings":["Tsinghua University,Department of Automation and BNRist,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation and BNRist,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.378,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.81802546,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1187","last_page":"1193"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9991999864578247,"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":0.9991999864578247,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.996399998664856,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9603000283241272,"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":0.7289228439331055},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7211310863494873},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.7201511263847351},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5819486975669861},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5234008431434631},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.449409157037735},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.43034207820892334},{"id":"https://openalex.org/keywords/bellman-equation","display_name":"Bellman equation","score":0.4223962128162384},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.42194420099258423},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.42178812623023987},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.41044193506240845},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.3404306173324585},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2598821520805359},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16462305188179016},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11895662546157837}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7289228439331055},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7211310863494873},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.7201511263847351},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5819486975669861},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5234008431434631},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.449409157037735},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.43034207820892334},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.4223962128162384},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42194420099258423},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.42178812623023987},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.41044193506240845},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3404306173324585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2598821520805359},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16462305188179016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11895662546157837},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci51031.2022.10022145","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci51031.2022.10022145","pdf_url":null,"source":{"id":"https://openalex.org/S4363605327","display_name":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","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 Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4055798484","display_name":null,"funder_award_id":"61936009","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":15,"referenced_works":["https://openalex.org/W1986225115","https://openalex.org/W2011483065","https://openalex.org/W2085871821","https://openalex.org/W2105470033","https://openalex.org/W2337617318","https://openalex.org/W2623693924","https://openalex.org/W2788517386","https://openalex.org/W2800510013","https://openalex.org/W2811338889","https://openalex.org/W2990867489","https://openalex.org/W2998165854","https://openalex.org/W3037410156","https://openalex.org/W3199516922","https://openalex.org/W4205808488","https://openalex.org/W4214717370"],"related_works":["https://openalex.org/W1985560493","https://openalex.org/W2386410636","https://openalex.org/W3096874164","https://openalex.org/W2357975469","https://openalex.org/W2937181779","https://openalex.org/W2808418668","https://openalex.org/W2025663273","https://openalex.org/W2145363145","https://openalex.org/W3168977894","https://openalex.org/W1574958246"],"abstract_inverted_index":{"Chinese":[0],"high-speed":[1,32,97],"railway":[2,33,98],"has":[3],"developed":[4],"rapidly":[5],"in":[6],"the":[7,14,23,27,31,36,54,68,90,95,104,133],"more":[8],"intelligent":[9],"and":[10,41,78,107,126],"automatic":[11],"direction":[12],"over":[13,121],"past":[15],"few":[16],"decades.":[17],"In":[18],"this":[19],"paper,":[20],"we":[21,51,66,93],"consider":[22,94],"optimization":[24],"problem":[25,70,77],"of":[26],"train":[28,38,55,108],"timetable":[29],"for":[30],"to":[34,88],"minimize":[35],"total":[37,42,123,128],"waiting":[39,124],"time":[40,110,125,130],"station":[43],"occupied":[44,129],"time.":[45],"To":[46],"deal":[47],"with":[48,132],"time-related":[49],"constraints,":[50],"first":[52],"establish":[53],"operation":[56],"environment":[57],"based":[58],"on":[59],"Discrete":[60],"Event":[61],"Dynamic":[62],"System":[63],"(DEDS).":[64],"Then,":[65],"reformulate":[67],"timetabling":[69],"as":[71,99],"a":[72,100],"Markov":[73],"Decision":[74],"Process":[75],"(MDP)":[76],"propose":[79],"an":[80],"improved":[81],"Q-learning":[82,118],"approach":[83],"by":[84],"redesigning":[85],"Q-value":[86],"function":[87],"solve":[89],"problem.":[91],"Finally,":[92],"Beijing-Shanghai":[96],"numerical":[101],"example,":[102],"where":[103],"passenger":[105],"flow":[106],"running":[109],"are":[111],"stochastic.":[112],"We":[113],"empirically":[114],"show":[115],"that":[116],"our":[117],"method":[119],"reduces":[120],"30%":[122],"1.9%":[127],"compared":[131],"well-known":[134],"First-Come-First-Service":[135],"(FCFS)":[136],"scheduling":[137],"strategy.":[138]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
