{"id":"https://openalex.org/W2988007379","doi":"https://doi.org/10.1109/access.2019.2950327","title":"Short-Term Passenger Flow Prediction Based on Wavelet Transform and Kernel Extreme Learning Machine","display_name":"Short-Term Passenger Flow Prediction Based on Wavelet Transform and Kernel Extreme Learning Machine","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2988007379","doi":"https://doi.org/10.1109/access.2019.2950327","mag":"2988007379"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2950327","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2950327","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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://doi.org/10.1109/access.2019.2950327","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103003842","display_name":"Ruijian Liu","orcid":"https://orcid.org/0000-0001-9667-0567"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruijian Liu","raw_affiliation_strings":["School of Economics and Management, Beijing University of Chemical Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100360653","display_name":"Yuhan Wang","orcid":"https://orcid.org/0000-0002-2942-5948"},"institutions":[{"id":"https://openalex.org/I4210123833","display_name":"Civil Aviation Management Institute of China","ror":"https://ror.org/03eh3c696","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210123833"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhan Wang","raw_affiliation_strings":["Department of Aviation Safety Management, Civil Aviation Management Institute of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Aviation Safety Management, Civil Aviation Management Institute of China, Beijing, China","institution_ids":["https://openalex.org/I4210123833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065543748","display_name":"Hong Zhou","orcid":"https://orcid.org/0000-0002-3146-2238"},"institutions":[{"id":"https://openalex.org/I2801441622","display_name":"China Railway Corporation","ror":"https://ror.org/044wv3489","country_code":"CN","type":"government","lineage":["https://openalex.org/I2801441622","https://openalex.org/I4210122102"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Zhou","raw_affiliation_strings":["Beijing Mass Transit Railway Operation Corporation, Ltd., Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Mass Transit Railway Operation Corporation, Ltd., Beijing, China","institution_ids":["https://openalex.org/I2801441622"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044553030","display_name":"Zeqiang Qian","orcid":"https://orcid.org/0000-0001-5299-1887"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeqiang Qian","raw_affiliation_strings":["School of Economics and Management, Beijing University of Chemical Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103003842"],"corresponding_institution_ids":["https://openalex.org/I75390827"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.9208,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.90185782,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"158025","last_page":"158034"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/extreme-learning-machine","display_name":"Extreme learning machine","score":0.7970366477966309},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7162437438964844},{"id":"https://openalex.org/keywords/urban-rail-transit","display_name":"Urban rail transit","score":0.6774354577064514},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5626219511032104},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5431226491928101},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5374287962913513},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5093570947647095},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.4662292003631592},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4492564797401428},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.37475383281707764},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32151246070861816},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1554926633834839},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09811478853225708}],"concepts":[{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.7970366477966309},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7162437438964844},{"id":"https://openalex.org/C2780434240","wikidata":"https://www.wikidata.org/wiki/Q3491904","display_name":"Urban rail transit","level":2,"score":0.6774354577064514},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5626219511032104},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5431226491928101},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5374287962913513},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5093570947647095},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.4662292003631592},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4492564797401428},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.37475383281707764},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32151246070861816},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1554926633834839},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09811478853225708},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2950327","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2950327","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:442759ae17fa4cffb4a7072274a6a121","is_oa":true,"landing_page_url":"https://doaj.org/article/442759ae17fa4cffb4a7072274a6a121","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"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 158025-158034 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2950327","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2950327","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1261612999","display_name":null,"funder_award_id":"2019M650456","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G6200642176","display_name":null,"funder_award_id":"71532003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W11316083","https://openalex.org/W180185937","https://openalex.org/W1973943669","https://openalex.org/W1982978808","https://openalex.org/W1988489815","https://openalex.org/W1994978245","https://openalex.org/W1995687640","https://openalex.org/W2002841906","https://openalex.org/W2003329977","https://openalex.org/W2014843617","https://openalex.org/W2017212187","https://openalex.org/W2026131661","https://openalex.org/W2027392238","https://openalex.org/W2049777773","https://openalex.org/W2103400647","https://openalex.org/W2111072639","https://openalex.org/W2111991989","https://openalex.org/W2125817951","https://openalex.org/W2131739422","https://openalex.org/W2131767615","https://openalex.org/W2132711183","https://openalex.org/W2160507653","https://openalex.org/W2168335399","https://openalex.org/W2171234954","https://openalex.org/W2213782059","https://openalex.org/W2317582298","https://openalex.org/W2325860717","https://openalex.org/W2413688291","https://openalex.org/W2460405629","https://openalex.org/W2529263555","https://openalex.org/W2613322775","https://openalex.org/W2731264215","https://openalex.org/W2794350824","https://openalex.org/W2799149976","https://openalex.org/W2801323363","https://openalex.org/W2805085159","https://openalex.org/W2887643628","https://openalex.org/W2889305768","https://openalex.org/W2894147476","https://openalex.org/W2898628748","https://openalex.org/W2914957400","https://openalex.org/W2946953362","https://openalex.org/W2969744824","https://openalex.org/W2975191766","https://openalex.org/W2981174025","https://openalex.org/W3017052951","https://openalex.org/W3180279812","https://openalex.org/W4232900813","https://openalex.org/W6607252422","https://openalex.org/W6728154360","https://openalex.org/W6768431404","https://openalex.org/W6798274656"],"related_works":["https://openalex.org/W2015747722","https://openalex.org/W2362050182","https://openalex.org/W2382418233","https://openalex.org/W2067443264","https://openalex.org/W2369897927","https://openalex.org/W3031731056","https://openalex.org/W4293167957","https://openalex.org/W2361035307","https://openalex.org/W2380455807","https://openalex.org/W2993975634"],"abstract_inverted_index":{"In":[0,37,128],"view":[1,38],"of":[2,7,33,39,63,107,114,136,141,186],"the":[3,17,20,25,64,112,115,121,133,146,151,164,170],"instability":[4],"and":[5,19,54,74,79,82,89,150,157,178,183],"complexity":[6],"passenger":[8,35,69],"flow":[9,70],"change":[10],"in":[11],"urban":[12,187],"rail":[13,188],"transit,":[14],"it":[15],"is":[16,66],"key":[18],"difficult":[21],"point":[22],"to":[23,28,67,87],"use":[24,84],"prediction":[26,97,126,134,171],"model":[27,47,65,123,138,153,166],"get":[29],"more":[30,176],"accurate":[31,177],"number":[32],"short-term":[34],"flow.":[36],"this,":[40],"this":[41,130],"study":[42,106],"proposes":[43],"a":[44,104,175],"hybrid":[45,152],"forecasting":[46],"W-KELM,":[48],"which":[49],"combines":[50],"wavelet":[51],"transform":[52],"(WT)":[53],"kernel":[55],"extreme":[56],"learning":[57],"machine":[58],"(KELM).":[59],"The":[60,117],"main":[61],"idea":[62],"decompose":[68],"data":[71],"into":[72],"high-frequency":[73],"low-frequency":[75],"sequences":[76,98],"through":[77],"WT":[78,156],"Mallat":[80],"algorithm,":[81],"then":[83],"KELM":[85,148],"approach":[86],"learn":[88],"forecast":[90],"signals":[91],"with":[92,139],"different":[93,96],"frequencies.":[94],"Finally,":[95],"are":[99],"reconstructed":[100],"using":[101],"WT.":[102],"Through":[103],"case":[105],"Beijing":[108],"metro,":[109],"we":[110],"test":[111],"effectiveness":[113],"model.":[116],"result":[118,135],"shows":[119,162],"that":[120,163],"W-KELM":[122,137,165],"has":[124],"good":[125],"accuracy.":[127,172],"addition,":[129],"paper":[131],"compare":[132],"those":[140],"BP":[142,158],"neural":[143,159],"network":[144],"model,":[145],"single":[147],"method,":[149],"based":[154],"on":[155],"network.":[160],"It":[161],"can":[167],"effectively":[168],"improve":[169],"Thus,":[173],"providing":[174],"real":[179],"situation":[180],"for":[181],"monitoring":[182],"early":[184],"warning":[185],"transit.":[189]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":4}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
