{"id":"https://openalex.org/W4205755030","doi":"https://doi.org/10.1109/tits.2021.3131248","title":"A Diverse Ensemble Deep Learning Method for Short-Term Traffic Flow Prediction Based on Spatiotemporal Correlations","display_name":"A Diverse Ensemble Deep Learning Method for Short-Term Traffic Flow Prediction Based on Spatiotemporal Correlations","publication_year":2021,"publication_date":"2021-12-03","ids":{"openalex":"https://openalex.org/W4205755030","doi":"https://doi.org/10.1109/tits.2021.3131248"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2021.3131248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3131248","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5101521482","display_name":"Yang Zhang","orcid":"https://orcid.org/0009-0008-2009-7280"},"institutions":[{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["School of Transportation, Fujian University of Technology, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-0432-5772","affiliations":[{"raw_affiliation_string":"School of Transportation, Fujian University of Technology, Fuzhou, China","institution_ids":["https://openalex.org/I83791580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058347995","display_name":"Dongrong Xin","orcid":null},"institutions":[{"id":"https://openalex.org/I83791580","display_name":"Fujian University of Technology","ror":"https://ror.org/03c8fdb16","country_code":"CN","type":"education","lineage":["https://openalex.org/I83791580"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongrong Xin","raw_affiliation_strings":["School of Civil Engineering, Fujian University of Technology, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7113-934X","affiliations":[{"raw_affiliation_string":"School of Civil Engineering, Fujian University of Technology, Fuzhou, China","institution_ids":["https://openalex.org/I83791580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101521482"],"corresponding_institution_ids":["https://openalex.org/I83791580"],"apc_list":null,"apc_paid":null,"fwci":1.6999,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.82827803,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"23","issue":"9","first_page":"16715","last_page":"16727"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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":1.0,"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/T10524","display_name":"Traffic control and management","score":0.9948999881744385,"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"}},{"id":"https://openalex.org/T10698","display_name":"Transportation Planning and Optimization","score":0.9912999868392944,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7343952655792236},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6640121936798096},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6025753021240234},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5945480465888977},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5848073363304138},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.57452392578125},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.5633406639099121},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5598623752593994},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5404958724975586},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4794394075870514},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.43820077180862427},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4308894872665405},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4260146915912628},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3584238886833191},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3550786077976227},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3487224578857422},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12769284844398499},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12279856204986572}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343952655792236},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6640121936798096},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6025753021240234},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5945480465888977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5848073363304138},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.57452392578125},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.5633406639099121},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5598623752593994},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5404958724975586},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4794394075870514},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.43820077180862427},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4308894872665405},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4260146915912628},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3584238886833191},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3550786077976227},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3487224578857422},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12769284844398499},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12279856204986572},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2021.3131248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3131248","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G272275932","display_name":null,"funder_award_id":"2019J01781","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"},{"id":"https://openalex.org/G3199264725","display_name":null,"funder_award_id":"2020J05194","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W170613937","https://openalex.org/W2002644193","https://openalex.org/W2004353783","https://openalex.org/W2012051283","https://openalex.org/W2076246037","https://openalex.org/W2131739422","https://openalex.org/W2145039203","https://openalex.org/W2165991108","https://openalex.org/W2389913108","https://openalex.org/W2521560026","https://openalex.org/W2573587735","https://openalex.org/W2613331518","https://openalex.org/W2756296971","https://openalex.org/W2761646508","https://openalex.org/W2769010373","https://openalex.org/W2792111260","https://openalex.org/W2793820729","https://openalex.org/W2804156429","https://openalex.org/W2809133492","https://openalex.org/W2913705552","https://openalex.org/W2913894912","https://openalex.org/W2939251941","https://openalex.org/W2964335123","https://openalex.org/W2965092899","https://openalex.org/W2988486362","https://openalex.org/W2996451395","https://openalex.org/W3026031135","https://openalex.org/W3113160599","https://openalex.org/W3189448883","https://openalex.org/W4250379029","https://openalex.org/W4403853345","https://openalex.org/W6759203762","https://openalex.org/W6986874642"],"related_works":["https://openalex.org/W2794896638","https://openalex.org/W2891633941","https://openalex.org/W3202800081","https://openalex.org/W3101614107","https://openalex.org/W1909207154","https://openalex.org/W4390971112","https://openalex.org/W3036530763","https://openalex.org/W1514365828","https://openalex.org/W3149839747","https://openalex.org/W3204228978"],"abstract_inverted_index":{"In":[0,164],"this":[1,151,174],"paper,":[2,152],"considering":[3],"spatiotemporal":[4,60],"correlations,":[5,129],"we":[6],"propose":[7],"a":[8,24,80,97,187,193],"novel":[9],"short-term":[10,49,167,209],"traffic":[11,38,63,168,182,210],"flow":[12,39,64,169,211],"prediction":[13,162,170,189,198,212],"method":[14,100,126,141,171],"that":[15,123,203],"is":[16,31,52,109,200],"based":[17],"on":[18,156],"diverse":[19,75,135],"ensemble":[20,72,107,131,143],"deep":[21],"learning.":[22],"First,":[23],"new":[25],"measurement":[26],"function":[27],"of":[28,37,58,62,74,82,93,105,127,134,142,160,204],"spatial":[29,35,128],"correlation":[30,36,61],"established,":[32],"quantifying":[33],"the":[34,42,55,59,69,91,94,102,106,113,116,124,130,138,158,161,166,181,197,205],"parameters.":[40,65],"Second,":[41],"convolutional":[43],"neural":[44],"network":[45,70],"model":[46],"embedded":[47],"long":[48],"memory":[50],"(LSTM-CNN)":[51],"used,":[53],"enhancing":[54],"learning":[56],"ability":[57],"Third,":[66],"according":[67],"to":[68,111,178],"structure,":[71],"rules":[73,133],"LSTM-CNNs":[76],"are":[77,148],"constructed,":[78],"combining":[79],"series":[81],"different":[83],"and":[84,89,137,196],"moderately":[85],"accurate":[86],"LSTM-CNN":[87,136],"models":[88],"improving":[90,157],"robustness":[92],"algorithm.":[95,163],"Finally,":[96],"dynamic":[98,139],"optimization":[99,140],"for":[101],"weight":[103,145],"parameters":[104],"elements":[108],"proposed":[110,149,172],"accommodate":[112],"changes":[114,179],"in":[115,150,173,180],"actual":[117],"road":[118],"networks.":[119],"The":[120],"experiments":[121],"show":[122],"measuring":[125],"combination":[132],"element":[144],"parameters,":[146],"which":[147],"have":[153],"positive":[154],"effects":[155],"performance":[159,199],"addition,":[165],"paper":[175],"can":[176,185],"adapt":[177],"flow.":[183],"It":[184],"obtain":[186],"better":[188,201],"effect":[190],"even":[191],"with":[192],"small":[194],"sample,":[195],"than":[202],"other":[206],"four":[207],"classical":[208],"methods.":[213]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
