{"id":"https://openalex.org/W2807798933","doi":"https://doi.org/10.3390/ijgi7060218","title":"A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting","display_name":"A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting","publication_year":2018,"publication_date":"2018-06-14","ids":{"openalex":"https://openalex.org/W2807798933","doi":"https://doi.org/10.3390/ijgi7060218","mag":"2807798933"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi7060218","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi7060218","pdf_url":"https://www.mdpi.com/2220-9964/7/6/218/pdf?version=1529075043","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/7/6/218/pdf?version=1529075043","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036257989","display_name":"Shifen Cheng","orcid":"https://orcid.org/0000-0002-9553-8318"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shifen Cheng","raw_affiliation_strings":["Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China","State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","University of Chinese Academy of Sciences, Beijing 100049, China"],"raw_orcid":"https://orcid.org/0000-0002-9553-8318","affiliations":[{"raw_affiliation_string":"Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China","institution_ids":[]},{"raw_affiliation_string":"State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101480749","display_name":"Feng Lu","orcid":"https://orcid.org/0000-0001-6573-2550"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210141657","display_name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application","ror":"https://ror.org/045yewh40","country_code":"CN","type":"facility","lineage":["https://openalex.org/I152031979","https://openalex.org/I4210141657"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Lu","raw_affiliation_strings":["Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China","Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China","State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","University of Chinese Academy of Sciences, Beijing 100049, China"],"raw_orcid":"https://orcid.org/0000-0001-6573-2550","affiliations":[{"raw_affiliation_string":"Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China","institution_ids":[]},{"raw_affiliation_string":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China","institution_ids":["https://openalex.org/I4210141657"]},{"raw_affiliation_string":"State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100416334","display_name":"Peng Peng","orcid":"https://orcid.org/0000-0003-2838-2111"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Peng","raw_affiliation_strings":["State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","University of Chinese Academy of Sciences, Beijing 100049, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China","institution_ids":["https://openalex.org/I4210160793","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101748387","display_name":"Sheng Wu","orcid":"https://orcid.org/0000-0001-7381-1937"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Wu","raw_affiliation_strings":["Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China","Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China","institution_ids":[]},{"raw_affiliation_string":"Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101480749"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210141657","https://openalex.org/I4210160793","https://openalex.org/I4210165038"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":2.3307,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.87448114,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"7","issue":"6","first_page":"218","last_page":"218"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9958999752998352,"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":0.9794999957084656,"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/closeness","display_name":"Closeness","score":0.7056236267089844},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6412784457206726},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6354411840438843},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5980834364891052},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5709694623947144},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.4917486011981964},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.49091464281082153},{"id":"https://openalex.org/keywords/spatial-correlation","display_name":"Spatial correlation","score":0.4861171245574951},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4612293541431427},{"id":"https://openalex.org/keywords/spatiotemporal-pattern","display_name":"Spatiotemporal pattern","score":0.43033507466316223},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3438130021095276},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34328603744506836},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14558732509613037},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13225534558296204}],"concepts":[{"id":"https://openalex.org/C2779545769","wikidata":"https://www.wikidata.org/wiki/Q5135364","display_name":"Closeness","level":2,"score":0.7056236267089844},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6412784457206726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6354411840438843},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5980834364891052},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5709694623947144},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.4917486011981964},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.49091464281082153},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.4861171245574951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4612293541431427},{"id":"https://openalex.org/C2779108282","wikidata":"https://www.wikidata.org/wiki/Q22908968","display_name":"Spatiotemporal pattern","level":2,"score":0.43033507466316223},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3438130021095276},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34328603744506836},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14558732509613037},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13225534558296204},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/ijgi7060218","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi7060218","pdf_url":"https://www.mdpi.com/2220-9964/7/6/218/pdf?version=1529075043","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:14af74191ef3495dbd6a78d0a7d61342","is_oa":true,"landing_page_url":"https://doaj.org/article/14af74191ef3495dbd6a78d0a7d61342","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":"ISPRS International Journal of Geo-Information, Vol 7, Iss 6, p 218 (2018)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2220-9964/7/6/218/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/ijgi7060218","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/ijgi7060218","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi7060218","pdf_url":"https://www.mdpi.com/2220-9964/7/6/218/pdf?version=1529075043","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2807798933.pdf"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W43001522","https://openalex.org/W1875626450","https://openalex.org/W1969865391","https://openalex.org/W1970460434","https://openalex.org/W1971741995","https://openalex.org/W1999464220","https://openalex.org/W2004073866","https://openalex.org/W2012497517","https://openalex.org/W2019836907","https://openalex.org/W2021153764","https://openalex.org/W2035577721","https://openalex.org/W2038206846","https://openalex.org/W2040297119","https://openalex.org/W2043279276","https://openalex.org/W2049952439","https://openalex.org/W2062017159","https://openalex.org/W2075407851","https://openalex.org/W2078019730","https://openalex.org/W2083316861","https://openalex.org/W2109764844","https://openalex.org/W2111991989","https://openalex.org/W2134460702","https://openalex.org/W2141961485","https://openalex.org/W2150010190","https://openalex.org/W2150498351","https://openalex.org/W2156793027","https://openalex.org/W2159889240","https://openalex.org/W2165991108","https://openalex.org/W2190353863","https://openalex.org/W2217607432","https://openalex.org/W2248601296","https://openalex.org/W2277710627","https://openalex.org/W2295737355","https://openalex.org/W2334631985","https://openalex.org/W2338714606","https://openalex.org/W2460404912","https://openalex.org/W2466025545","https://openalex.org/W2521007984","https://openalex.org/W2528639018","https://openalex.org/W2579495707","https://openalex.org/W2604472983","https://openalex.org/W2613331518","https://openalex.org/W2684846153","https://openalex.org/W2766790689","https://openalex.org/W2789788750","https://openalex.org/W3104764698","https://openalex.org/W6702948470","https://openalex.org/W6728254797"],"related_works":["https://openalex.org/W1995054232","https://openalex.org/W2156910174","https://openalex.org/W1556709767","https://openalex.org/W2011510925","https://openalex.org/W1557920161","https://openalex.org/W1993023208","https://openalex.org/W4291020658","https://openalex.org/W2593813644","https://openalex.org/W1562218326","https://openalex.org/W2061476331"],"abstract_inverted_index":{"Short-term":[0],"traffic":[1,22,32,107,124,161,177,188,240],"forecasting":[2,23,108,241],"plays":[3],"an":[4,99],"important":[5],"part":[6],"in":[7,24,83,179,305],"intelligent":[8],"transportation":[9],"systems.":[10],"Spatiotemporal":[11],"k-nearest":[12,102],"neighbor":[13,103],"models":[14,38,57],"(ST-KNNs)":[15],"have":[16],"been":[17],"widely":[18],"adopted":[19],"for":[20,105,130,253,263,276],"short-term":[21,106,239],"which":[25,81,287],"spatiotemporal":[26,53,101,121,144,151,165,182,194,209],"matrices":[27,156],"are":[28,134,146,196],"constructed":[29,150],"to":[30,42,64,157,174,204,289],"describe":[31,205],"conditions.":[33],"The":[34,267],"performance":[35],"of":[36,52,79,93,123,187,208,293],"the":[37,43,46,50,70,76,90,120,127,149,171,180,185,206,230,234,244,254,264],"is":[39,168],"closely":[40],"related":[41],"spatial":[44,66,128,294],"dependencies,":[45,48],"temporal":[47,71,77,141,301],"and":[49,61,68,154,223,251,261,284,296,303],"interaction":[51,72,207],"dependencies.":[54,142,210],"However,":[55],"these":[56,192],"use":[58],"distance":[59],"functions":[60],"correlation":[62,295],"coefficients":[63],"identify":[65],"neighbors":[67,129],"measure":[69],"by":[73,109,198,242],"only":[74],"considering":[75],"closeness":[78],"traffic,":[80],"result":[82],"existing":[84],"ST-KNNs":[85],"that":[86,117,233,270],"cannot":[87],"fully":[88,118],"reflect":[89],"essential":[91],"features":[92],"road":[94,132,256,306],"traffic.":[95,307],"This":[96],"study":[97],"proposes":[98],"improved":[100],"model":[104,173,236],"utilizing":[110],"a":[111,164,200,282],"multi-view":[112,271],"learning":[113,272],"algorithm":[114,203],"named":[115],"MVL-STKNN":[116,235],"considers":[119],"dependencies":[122],"data.":[125],"First,":[126],"each":[131],"segment":[133],"automatically":[135],"determined":[136],"using":[137,199,215],"cross-correlation":[138],"under":[139,191,280],"different":[140],"Three":[143],"views":[145,195],"built":[147],"on":[148,220],"closeness,":[152],"periodic,":[153],"trend":[155],"represent":[158],"spatially":[159],"heterogeneous":[160],"states.":[162],"Second,":[163],"weighting":[166],"matrix":[167],"introduced":[169],"into":[170],"ST-KNN":[172],"recognize":[175],"similar":[176],"patterns":[178],"three":[181,193],"views.":[183],"Finally,":[184],"results":[186,231,268],"pattern":[189],"recognition":[190],"aggregated":[197],"neural":[201],"network":[202],"Extensive":[211],"experiments":[212],"were":[213],"conducted":[214],"real":[216],"vehicular-speed":[217],"datasets":[218],"collected":[219],"city":[221,255],"roads":[222],"expressways.":[224],"In":[225],"comparison":[226],"with":[227],"baseline":[228],"methods,":[229],"show":[232],"greatly":[237],"improves":[238],"lowering":[243],"mean":[245],"absolute":[246],"percentage":[247],"error":[248],"between":[249,259],"28.24%":[250],"46.86%":[252],"dataset":[257],"and,":[258],"53.80%":[260],"90.29%,":[262],"expressway":[265],"dataset.":[266],"suggest":[269],"merits":[273],"further":[274],"attention":[275],"traffic-related":[277],"data":[278],"mining":[279],"such":[281],"dynamic":[283],"data-intensive":[285],"environment,":[286],"owes":[288],"its":[290],"comprehensive":[291],"consideration":[292],"heterogeneity":[297],"as":[298,300],"well":[299],"fluctuation":[302],"regularity":[304]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":7}],"updated_date":"2026-06-20T22:02:38.213706","created_date":"2025-10-10T00:00:00"}
