{"id":"https://openalex.org/W2790566879","doi":"https://doi.org/10.1109/itsc.2017.8317609","title":"Prediction algorithms for train arrival time in urban rail transit","display_name":"Prediction algorithms for train arrival time in urban rail transit","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2790566879","doi":"https://doi.org/10.1109/itsc.2017.8317609","mag":"2790566879"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2017.8317609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th 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/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":["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/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":["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/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":["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":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9994999766349792,"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"}},{"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/T11568","display_name":"Railway Systems and Energy Efficiency","score":0.9955000281333923,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/arrival-time","display_name":"Arrival time","score":0.7706917524337769},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6462064385414124},{"id":"https://openalex.org/keywords/headway","display_name":"Headway","score":0.6393688917160034},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6377079486846924},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6288406848907471},{"id":"https://openalex.org/keywords/time-of-arrival","display_name":"Time of arrival","score":0.5386597514152527},{"id":"https://openalex.org/keywords/dwell-time","display_name":"Dwell time","score":0.498734712600708},{"id":"https://openalex.org/keywords/workload","display_name":"Workload","score":0.4483564496040344},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.418434202671051},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28386107087135315},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.19058039784431458},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18823271989822388},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.13721299171447754}],"concepts":[{"id":"https://openalex.org/C3017552255","wikidata":"https://www.wikidata.org/wiki/Q4135208","display_name":"Arrival time","level":2,"score":0.7706917524337769},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6462064385414124},{"id":"https://openalex.org/C2779240695","wikidata":"https://www.wikidata.org/wiki/Q4383682","display_name":"Headway","level":2,"score":0.6393688917160034},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6377079486846924},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6288406848907471},{"id":"https://openalex.org/C163150518","wikidata":"https://www.wikidata.org/wiki/Q4135208","display_name":"Time of arrival","level":3,"score":0.5386597514152527},{"id":"https://openalex.org/C151637689","wikidata":"https://www.wikidata.org/wiki/Q5318064","display_name":"Dwell time","level":2,"score":0.498734712600708},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.4483564496040344},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.418434202671051},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28386107087135315},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.19058039784431458},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18823271989822388},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.13721299171447754},{"id":"https://openalex.org/C70410870","wikidata":"https://www.wikidata.org/wiki/Q199906","display_name":"Clinical psychology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2017.8317609","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317609","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8100000023841858,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W630611528","https://openalex.org/W652490728","https://openalex.org/W1988604380","https://openalex.org/W2026184121","https://openalex.org/W2039559615","https://openalex.org/W2044936056","https://openalex.org/W2087836750","https://openalex.org/W2110694256","https://openalex.org/W2362730819","https://openalex.org/W2366009691","https://openalex.org/W2367212349","https://openalex.org/W2372049907","https://openalex.org/W2379299365","https://openalex.org/W2381007720","https://openalex.org/W2385071838","https://openalex.org/W2568077636","https://openalex.org/W6620158162","https://openalex.org/W6621700157","https://openalex.org/W6709762068","https://openalex.org/W6731452511","https://openalex.org/W7025157116"],"related_works":["https://openalex.org/W2604757976","https://openalex.org/W2337988829","https://openalex.org/W2765924402","https://openalex.org/W2920514105","https://openalex.org/W4288481730","https://openalex.org/W4387960969","https://openalex.org/W2550892593","https://openalex.org/W3003184106","https://openalex.org/W3028083027","https://openalex.org/W4387264970"],"abstract_inverted_index":{"In":[0],"urban":[1],"rail":[2],"transit,":[3],"the":[4,25,32,39,48,97],"existing":[5],"methods":[6],"used":[7,92],"for":[8,19,51,74,83,93,121],"arrival":[9,21,35,53,123],"time":[10,36,72,81],"prediction":[11,73,82,119],"have":[12],"low":[13],"accuracy.":[14,127],"A":[15,107],"high-precision":[16],"predictive":[17,33,49,98],"method":[18],"train":[20,28,52,89,122],"times":[22,54,124],"can":[23],"reduce":[24],"workload":[26],"of":[27,41,102],"drivers":[29],"by":[30],"providing":[31],"terminal":[34],"and":[37,63,79,95],"satisfy":[38],"requirement":[40],"headway":[42],"control":[43],"model.":[44],"This":[45],"paper":[46],"proposes":[47],"algorithms":[50,67,104,111,120],"using":[55],"back-propagation":[56],"(BP)":[57],"neural":[58,61],"network,":[59],"wavelet":[60],"network":[62],"genetic":[64],"algorithm.":[65],"These":[66],"include":[68],"two":[69,77],"parts:":[70],"running":[71],"sections":[75],"between":[76,109],"stations":[78],"dwell":[80],"stations.":[84],"The":[85,100,114],"real":[86],"data":[87],"on":[88],"operation":[90],"are":[91],"training":[94],"testing":[96],"algorithms.":[99],"feasibility":[101],"these":[103,110,118],"is":[105,112],"validated.":[106],"comparison":[108],"given.":[113],"results":[115],"prove":[116],"that":[117],"achieve":[125],"high":[126]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
