{"id":"https://openalex.org/W2905258889","doi":"https://doi.org/10.1109/itsc.2018.8569399","title":"An intelligent train control approach based on the monte carlo reinforcement learning algorithm","display_name":"An intelligent train control approach based on the monte carlo reinforcement learning algorithm","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2905258889","doi":"https://doi.org/10.1109/itsc.2018.8569399","mag":"2905258889"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2018.8569399","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st 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/A5100451391","display_name":"Wentao Liu","orcid":"https://orcid.org/0000-0001-9627-0168"},"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":"Wentao Liu","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"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/A5067908158","display_name":"Tao Tang","orcid":"https://orcid.org/0000-0001-7838-8525"},"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":"Tao Tang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China"],"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/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"],"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/A5015347934","display_name":"Yuan Cao","orcid":"https://orcid.org/0000-0001-6631-4908"},"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":"Yuan Cao","raw_affiliation_strings":["National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100690446","display_name":"Feng Bao","orcid":"https://orcid.org/0000-0001-9460-4339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng Bao","raw_affiliation_strings":["Traffic Control Technology Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Traffic Control Technology Co., Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035216826","display_name":"Jianguo Gao","orcid":"https://orcid.org/0000-0002-3437-4604"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianguo Gao","raw_affiliation_strings":["Traffic Control Technology Co., Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Traffic Control Technology Co., Ltd., Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100451391"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.7391,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.79052475,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10842","display_name":"Railway Engineering and Dynamics","score":0.9779999852180481,"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/T12371","display_name":"Electrical Contact Performance and Analysis","score":0.9686999917030334,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8391785025596619},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7170089483261108},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6719672679901123},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5047262907028198},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.48273178935050964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4734327793121338},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4432946741580963},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4112364947795868},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3551960587501526},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3400416374206543},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08224159479141235}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8391785025596619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7170089483261108},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6719672679901123},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5047262907028198},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.48273178935050964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4734327793121338},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4432946741580963},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4112364947795868},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3551960587501526},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3400416374206543},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08224159479141235},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2018.8569399","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W40014655","https://openalex.org/W1852135537","https://openalex.org/W1853065930","https://openalex.org/W1974920357","https://openalex.org/W1988986641","https://openalex.org/W2009975158","https://openalex.org/W2038814776","https://openalex.org/W2042112108","https://openalex.org/W2044936056","https://openalex.org/W2051937592","https://openalex.org/W2057211133","https://openalex.org/W2058774661","https://openalex.org/W2065474320","https://openalex.org/W2109028470","https://openalex.org/W2121863487","https://openalex.org/W2128727377","https://openalex.org/W2131460616","https://openalex.org/W2155257334","https://openalex.org/W2346925403","https://openalex.org/W2500222322","https://openalex.org/W2533696363","https://openalex.org/W2624746261","https://openalex.org/W2723208868","https://openalex.org/W6660972995"],"related_works":["https://openalex.org/W3074294383","https://openalex.org/W4206669594","https://openalex.org/W2961085424","https://openalex.org/W2959276766","https://openalex.org/W4295941380","https://openalex.org/W260766989","https://openalex.org/W3139193008","https://openalex.org/W4306674287","https://openalex.org/W3111983280","https://openalex.org/W4319083788"],"abstract_inverted_index":{"Traditional":[0],"research":[1],"on":[2,126],"the":[3,8,13,19,23,36,40,70,74,77,80,86,93,103,107,119,122,127],"train":[4,10,31,49,62,87,108],"control":[5,14,32,109],"problem":[6],"use":[7],"determined":[9],"model":[11],"and":[12,39],"method":[15,43,55,98],"cannot":[16],"deal":[17],"with":[18],"variable":[20],"parameters":[21],"in":[22],"complex":[24],"environment.":[25],"This":[26],"paper":[27],"proposes":[28],"a":[29,47,52,112],"new":[30],"approach":[33,124],"by":[34],"analyzing":[35],"operation":[37,63,78,88,129],"data":[38,130],"reinforcement":[41,96],"learning":[42,97],"instead":[44],"of":[45,61,73,82,121,131],"using":[46],"detailed":[48],"model.":[50],"Specifically,":[51],"similarity-based":[53],"sampling":[54],"is":[56,66,99,115],"introduced":[57],"to":[58,68,101,117],"sample":[59],"segments":[60],"data,":[64,79],"which":[65],"used":[67],"predict":[69],"running":[71],"state":[72],"train.":[75],"With":[76],"reward":[81],"each":[83],"segment":[84],"for":[85,106],"can":[89],"be":[90],"evaluated.":[91],"Then,":[92],"monte":[94],"carlo":[95],"applied":[100],"select":[102],"optimal":[104],"solution":[105],"decision.":[110],"Finally,":[111],"case":[113],"study":[114],"conducted":[116],"illustrate":[118],"effectiveness":[120],"proposed":[123],"based":[125],"practical":[128],"Yizhuang":[132],"line.":[133]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}