{"id":"https://openalex.org/W3116060049","doi":"https://doi.org/10.1109/itsc45102.2020.9294245","title":"An Energy-Efficient Train Control Approach Based on Deep Q-Network Methodology","display_name":"An Energy-Efficient Train Control Approach Based on Deep Q-Network Methodology","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3116060049","doi":"https://doi.org/10.1109/itsc45102.2020.9294245","mag":"3116060049"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100337585","display_name":"Chen Wang","orcid":"https://orcid.org/0000-0002-4749-8091"},"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":"Chen Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100451405","display_name":"Wentao Liu","orcid":"https://orcid.org/0000-0002-4241-5170"},"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":"Wentao Liu","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/A5082360285","display_name":"Qinghao Tian","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":"Qinghao Tian","raw_affiliation_strings":["School of Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038996257","display_name":"Shuai Su","orcid":"https://orcid.org/0000-0001-8412-9853"},"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":"Shuai Su","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/A5101796944","display_name":"Miao Zhang","orcid":"https://orcid.org/0000-0001-5490-1283"},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miao Zhang","raw_affiliation_strings":["China Academy of Railway Sciences Corporation Limited"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Academy of Railway Sciences Corporation Limited","institution_ids":["https://openalex.org/I4210141966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":null,"first_page":"1","last_page":"6"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9891999959945679,"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":0.986299991607666,"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/punctuality","display_name":"Punctuality","score":0.8870859146118164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7505254149436951},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6874974370002747},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.6735652089118958},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5844231247901917},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.5600559711456299},{"id":"https://openalex.org/keywords/optimal-control","display_name":"Optimal control","score":0.521093487739563},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4747740924358368},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.43679094314575195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37171685695648193},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.35942959785461426},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.24641630053520203},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1865231692790985}],"concepts":[{"id":"https://openalex.org/C2779548549","wikidata":"https://www.wikidata.org/wiki/Q153487","display_name":"Punctuality","level":2,"score":0.8870859146118164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505254149436951},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6874974370002747},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.6735652089118958},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5844231247901917},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.5600559711456299},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.521093487739563},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4747740924358368},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.43679094314575195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37171685695648193},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.35942959785461426},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.24641630053520203},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1865231692790985},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc45102.2020.9294245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.9200000166893005,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323067","display_name":"State Key Laboratory of Rail Traffic Control and Safety","ror":"https://ror.org/01yj56c84"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W41554520","https://openalex.org/W1571587569","https://openalex.org/W1603678020","https://openalex.org/W1988986641","https://openalex.org/W2087025933","https://openalex.org/W2091098082","https://openalex.org/W2109028470","https://openalex.org/W2109879495","https://openalex.org/W2111593151","https://openalex.org/W2121863487","https://openalex.org/W2131460616","https://openalex.org/W2145339207","https://openalex.org/W2533696363","https://openalex.org/W2976327234","https://openalex.org/W6635948928","https://openalex.org/W6768190797"],"related_works":["https://openalex.org/W2766395386","https://openalex.org/W2376773662","https://openalex.org/W428785106","https://openalex.org/W4313598038","https://openalex.org/W2068695701","https://openalex.org/W2782149065","https://openalex.org/W1595000447","https://openalex.org/W2053811733","https://openalex.org/W2347564323","https://openalex.org/W2332851221"],"abstract_inverted_index":{"Application":[0],"of":[1,15,21,75,95,127,160],"the":[2,9,13,22,29,33,43,47,66,72,79,89,93,108,115,125,136,149,158,161],"automatic":[3],"train":[4,48,67,76,107],"operation":[5],"can":[6,64,123,140],"efficiently":[7],"reduce":[8],"energy":[10,90],"consumption":[11,91],"under":[12,92],"premise":[14],"ensuring":[16],"safety":[17],"and":[18,78,98],"punctuality.":[19],"Most":[20],"traditional":[23],"research":[24],"mainly":[25],"focused":[26],"on":[27,148],"solving":[28],"driving":[30,102],"strategy":[31,69,139],"with":[32,130],"given":[34],"model":[35],"or":[36],"fixed":[37],"parameters.":[38],"To":[39],"flexibly":[40],"respond":[41],"to":[42,87,106,144,156],"dynamic":[44],"changes":[45],"during":[46],"operation,":[49],"this":[50],"paper":[51],"proposes":[52],"a":[53,58],"data-driven":[54],"based":[55],"approach,":[56,62],"specifically":[57],"reinforcement":[59],"learning":[60],"(RL)":[61],"which":[63],"optimize":[65],"control":[68,138],"without":[70],"using":[71],"prior":[73],"knowledge":[74],"dynamics":[77],"predesigned":[80],"velocity":[81],"profile.":[82],"The":[83,119],"optimization":[84],"objective":[85],"is":[86],"minimize":[88],"constraints":[94],"punctual":[96],"arrival":[97],"speed":[99],"restrictions.":[100],"Numerous":[101],"experiences":[103],"are":[104,154],"used":[105],"deep":[109],"Q-network":[110],"(DQN)":[111],"until":[112],"it":[113],"approximates":[114],"optimal":[116],"action-value":[117],"function.":[118],"trained":[120],"neural":[121],"network":[122],"output":[124],"values":[126],"all":[128],"actions":[129],"arbitrary":[131],"input":[132],"state":[133],"such":[134],"that":[135],"energy-efficient":[137],"be":[141],"obtained":[142],"according":[143],"action":[145],"values.":[146],"Based":[147],"real-world":[150],"data,":[151],"some":[152],"cases":[153],"conducted":[155],"illustrate":[157],"effectiveness":[159],"proposed":[162],"approach.":[163]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
