{"id":"https://openalex.org/W2927719797","doi":"https://doi.org/10.1109/access.2019.2907739","title":"Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM","display_name":"Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2927719797","doi":"https://doi.org/10.1109/access.2019.2907739","mag":"2927719797"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2907739","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2907739","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675737.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675737.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044243403","display_name":"Jianyuan Guo","orcid":"https://orcid.org/0000-0003-4093-0459"},"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":"Jianyuan Guo","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4093-0459","affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112125359","display_name":"Zhen Xie","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":"Zhen Xie","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088716214","display_name":"Yong Qin","orcid":"https://orcid.org/0000-0002-6519-8316"},"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":"Yong Qin","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/A5108050300","display_name":"Limin Jia","orcid":"https://orcid.org/0000-0002-1561-0405"},"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":"Limin Jia","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/A5099243640","display_name":"Yaguan Wang","orcid":"https://orcid.org/0000-0001-7710-5861"},"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":"Yaguan Wang","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, 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":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":10.459,"has_fulltext":true,"cited_by_count":136,"citation_normalized_percentile":{"value":0.98801872,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"7","issue":null,"first_page":"42946","last_page":"42955"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.996999979019165,"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/T10370","display_name":"Traffic and Road Safety","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.7756919860839844},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6586222648620605},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.573016345500946},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5428230166435242},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5423995852470398},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.5166012048721313},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4784157872200012},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4723305106163025},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4670044183731079},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.46644091606140137},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4379636347293854},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43678438663482666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39601296186447144},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34634050726890564},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3457929491996765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0749521255493164}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7756919860839844},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6586222648620605},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.573016345500946},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5428230166435242},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5423995852470398},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.5166012048721313},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4784157872200012},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4723305106163025},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4670044183731079},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.46644091606140137},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4379636347293854},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43678438663482666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39601296186447144},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34634050726890564},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3457929491996765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0749521255493164},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2907739","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2907739","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675737.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:502cec6b376f4ae3bb429ff02386c115","is_oa":true,"landing_page_url":"https://doaj.org/article/502cec6b376f4ae3bb429ff02386c115","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 42946-42955 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2907739","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2907739","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08675737.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8299999833106995,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2140235226","display_name":null,"funder_award_id":"2016YFB1200402","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W252542266","https://openalex.org/W1981346203","https://openalex.org/W1987728022","https://openalex.org/W1995687640","https://openalex.org/W2002841906","https://openalex.org/W2004353783","https://openalex.org/W2005436527","https://openalex.org/W2029050814","https://openalex.org/W2036785686","https://openalex.org/W2049289224","https://openalex.org/W2049604381","https://openalex.org/W2049952439","https://openalex.org/W2064675550","https://openalex.org/W2069929199","https://openalex.org/W2075952974","https://openalex.org/W2085987121","https://openalex.org/W2100649405","https://openalex.org/W2125817951","https://openalex.org/W2139606794","https://openalex.org/W2170785963","https://openalex.org/W2176173702","https://openalex.org/W2217607432","https://openalex.org/W2352692987","https://openalex.org/W2380385090","https://openalex.org/W2460842349","https://openalex.org/W2531059648","https://openalex.org/W2544770515","https://openalex.org/W2554404266","https://openalex.org/W2575351252","https://openalex.org/W2582331423","https://openalex.org/W2588759037","https://openalex.org/W2593182953","https://openalex.org/W2695874637","https://openalex.org/W2751650042","https://openalex.org/W2782738497","https://openalex.org/W2791609897","https://openalex.org/W2793820729","https://openalex.org/W2800123561","https://openalex.org/W2800460440","https://openalex.org/W2891280833","https://openalex.org/W2916664939","https://openalex.org/W6759360321"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W3032952384","https://openalex.org/W3017902212","https://openalex.org/W2964335273","https://openalex.org/W2982145560","https://openalex.org/W4385335406"],"abstract_inverted_index":{"Passenger":[0],"flow":[1,17,92],"prediction":[2,13,104,124],"is":[3,18,44,77,106],"important":[4],"for":[5],"the":[6,30,49,52,58,64,70,81,87,102,110,119,123,129,133,139,154],"operation":[7],"of":[8,14,32,48,57,90,112,122,128,142],"urban":[9],"rail":[10],"transit.":[11],"The":[12,46,126],"abnormal":[15,53,91,140],"passenger":[16,143],"difficult":[19],"due":[20],"to":[21,79],"rare":[22],"similar":[23],"history":[24],"data.":[25],"A":[26,73,97],"model":[27,50,135],"based":[28,68,100],"on":[29,69,101,108],"fusion":[31],"support":[33],"vector":[34],"regression":[35],"(SVR)":[36],"and":[37,63,95,114,148],"long":[38],"short-term":[39],"memory":[40],"(LSTM)":[41],"neural":[42],"network":[43],"proposed.":[45],"inputs":[47],"are":[51,116],"features,":[54],"which":[55,84,109,145],"consist":[56],"recent":[59],"real":[60],"volume":[61,66],"series":[62,67],"predicted":[65],"periodic":[71],"features.":[72],"two-stage":[74],"training":[75],"method":[76,99],"designed":[78],"train":[80],"LSTM":[82,115],"model,":[83],"can":[85],"reflect":[86],"large":[88],"fluctuations":[89,141],"more":[93,136],"timely":[94],"approximately.":[96],"combination":[98],"real-time":[103],"errors":[105],"proposed,":[107],"outputs":[111,121],"SVR":[113],"combined":[117],"into":[118],"final":[120],"model.":[125],"results":[127],"experiments":[130],"show":[131],"that":[132],"SVR-LSTM":[134],"accurately":[137],"reflects":[138],"flow,":[144],"performs":[146],"well":[147],"yields":[149],"greater":[150],"forecast":[151],"accuracy":[152],"than":[153],"individual":[155],"models.":[156]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":34},{"year":2022,"cited_by_count":21},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":31},{"year":2019,"cited_by_count":2}],"updated_date":"2026-05-19T21:40:30.786675","created_date":"2025-10-10T00:00:00"}