{"id":"https://openalex.org/W2990867489","doi":"https://doi.org/10.1109/itsc.2019.8917180","title":"A Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling under Disturbances","display_name":"A Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling under Disturbances","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2990867489","doi":"https://doi.org/10.1109/itsc.2019.8917180","mag":"2990867489"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2019.8917180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (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/A5074198390","display_name":"Lingbin Ning","orcid":"https://orcid.org/0000-0002-0957-2959"},"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":"Lingbin Ning","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010019122","display_name":"Yidong Li","orcid":"https://orcid.org/0000-0003-2965-6196"},"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":"Yidong Li","raw_affiliation_strings":["School of the Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of the Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090290928","display_name":"Min Zhou","orcid":"https://orcid.org/0000-0002-9286-1726"},"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":"Min Zhou","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032079147","display_name":"Haifeng Song","orcid":"https://orcid.org/0000-0001-9840-5804"},"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":"Haifeng Song","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046146732","display_name":"Hairong Dong","orcid":"https://orcid.org/0000-0003-4369-3401"},"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":"Hairong Dong","raw_affiliation_strings":["State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":7.4641,"has_fulltext":false,"cited_by_count":84,"citation_normalized_percentile":{"value":0.97374452,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3469","last_page":"3474"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9883999824523926,"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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9848999977111816,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.8868874311447144},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8408239483833313},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6441875100135803},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5537357926368713},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46870607137680054},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4427301585674286},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3517504334449768},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09776297211647034}],"concepts":[{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.8868874311447144},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8408239483833313},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6441875100135803},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5537357926368713},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46870607137680054},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4427301585674286},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3517504334449768},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09776297211647034},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/itsc.2019.8917180","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917180","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1757796397","https://openalex.org/W1954876655","https://openalex.org/W2007450520","https://openalex.org/W2033454236","https://openalex.org/W2034008299","https://openalex.org/W2051578522","https://openalex.org/W2063735682","https://openalex.org/W2134131633","https://openalex.org/W2337617318","https://openalex.org/W2508428251","https://openalex.org/W2572262102","https://openalex.org/W2580175322","https://openalex.org/W2596128244","https://openalex.org/W2623693924","https://openalex.org/W2765787256","https://openalex.org/W2766447205","https://openalex.org/W2790697085","https://openalex.org/W3103559770","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6732665253"],"related_works":["https://openalex.org/W618248309","https://openalex.org/W2377336366","https://openalex.org/W1568097102","https://openalex.org/W4390419160","https://openalex.org/W1601203902","https://openalex.org/W2075798043","https://openalex.org/W2102464536","https://openalex.org/W4225671779","https://openalex.org/W2361332776","https://openalex.org/W2379583742"],"abstract_inverted_index":{"Train":[0],"timetable":[1,100],"rescheduling":[2],"(TTR)":[3],"aims":[4],"to":[5,32,57,128],"address":[6],"the":[7,34,42,46,69,104,114,118,129],"recovery":[8],"of":[9],"train":[10,48],"operation":[11,49],"order":[12],"in":[13,50,124],"reordering":[14],"and":[15,53,68,83,88],"retiming":[16],"strategies":[17],"during":[18],"disturbances.":[19],"Considering":[20],"this":[21,23],"problem,":[22],"paper":[24],"introduces":[25],"a":[26,59],"deep":[27],"reinforcement":[28],"learning":[29,60,73],"(DRL)":[30],"approach":[31,116],"minimize":[33],"average":[35,119],"total":[36,120],"delay":[37,121],"for":[38,77,86],"all":[39],"trains":[40,87],"along":[41],"railway":[43,107],"line.":[44,108],"Specifically,":[45],"detailed":[47],"block":[51],"sections":[52],"stations":[54],"is":[55,75],"illustrated":[56],"establish":[58],"environment":[61],"involving":[62],"its":[63],"state":[64],"sets,":[65,67],"action":[66],"reward":[70],"function.":[71],"The":[72,109],"agent":[74],"responsible":[76],"adjusting":[78],"running":[79],"times,":[80],"dwell":[81],"times":[82],"departure":[84],"sequences":[85],"conflicts":[89],"are":[90,95],"resolved":[91],"simultaneously.":[92],"Numerical":[93],"experiments":[94],"performed":[96],"on":[97,103],"an":[98],"adapted":[99],"carried":[101],"out":[102],"Beijing-Shanghai":[105],"high-speed":[106],"experimental":[110],"results":[111],"indicate":[112],"that":[113],"proposed":[115],"reduces":[117],"by":[122],"46.38%":[123],"real":[125],"time,":[126],"compared":[127],"First-Come-First-Served":[130],"(FCFS)":[131],"method.":[132]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}