{"id":"https://openalex.org/W2894714913","doi":"https://doi.org/10.1109/tits.2018.2869768","title":"Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method","display_name":"Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2894714913","doi":"https://doi.org/10.1109/tits.2018.2869768","mag":"2894714913"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2018.2869768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2869768","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/A5100445638","display_name":"Linchao Li","orcid":"https://orcid.org/0000-0002-2574-0174"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linchao Li","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0002-2574-0174","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100410024","display_name":"Jian Zhang","orcid":"https://orcid.org/0000-0002-9086-7622"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Zhang","raw_affiliation_strings":["School of Transportation, Southeast University, Nanjing, Jiangsu, China"],"raw_orcid":"https://orcid.org/0000-0002-9086-7622","affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Nanjing, Jiangsu, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100334354","display_name":"Yonggang Wang","orcid":"https://orcid.org/0000-0002-9365-1851"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonggang Wang","raw_affiliation_strings":["School of Highway, Chang'an University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-9365-1851","affiliations":[{"raw_affiliation_string":"School of Highway, Chang'an University, Xi'an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060394098","display_name":"Bin Ran","orcid":"https://orcid.org/0000-0002-5464-0930"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Ran","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI, USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":14.0121,"has_fulltext":false,"cited_by_count":197,"citation_normalized_percentile":{"value":0.99372027,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"20","issue":"8","first_page":"2933","last_page":"2943"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9945999979972839,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9894000291824341,"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/missing-data","display_name":"Missing data","score":0.8227771520614624},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.7947194576263428},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6808224320411682},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.67716383934021},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.49378880858421326},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.41376349329948425},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37731704115867615},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3542325794696808}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8227771520614624},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.7947194576263428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6808224320411682},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.67716383934021},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.49378880858421326},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.41376349329948425},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37731704115867615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3542325794696808}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2018.2869768","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2869768","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W791265519","https://openalex.org/W1798398164","https://openalex.org/W1971757341","https://openalex.org/W1979646154","https://openalex.org/W1981982250","https://openalex.org/W2004353783","https://openalex.org/W2005248249","https://openalex.org/W2011925575","https://openalex.org/W2017917631","https://openalex.org/W2019191255","https://openalex.org/W2020641160","https://openalex.org/W2031069824","https://openalex.org/W2033291788","https://openalex.org/W2039015671","https://openalex.org/W2039417141","https://openalex.org/W2045593919","https://openalex.org/W2047627251","https://openalex.org/W2049500727","https://openalex.org/W2051299429","https://openalex.org/W2064675550","https://openalex.org/W2078994812","https://openalex.org/W2100235918","https://openalex.org/W2163150789","https://openalex.org/W2179076887","https://openalex.org/W2190353863","https://openalex.org/W2334686861","https://openalex.org/W2342643507","https://openalex.org/W2343462218","https://openalex.org/W2465297350","https://openalex.org/W2469618837","https://openalex.org/W2528179341","https://openalex.org/W2529827714","https://openalex.org/W2547812180","https://openalex.org/W2583110309","https://openalex.org/W2768134894","https://openalex.org/W2989457786","https://openalex.org/W3146166473","https://openalex.org/W4241115065","https://openalex.org/W6720065314"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549","https://openalex.org/W3123177881"],"abstract_inverted_index":{"In":[0,39],"reality,":[1],"readings":[2],"of":[3,97,125,131,159,175],"sensors":[4],"on":[5],"highways":[6],"are":[7,103,119],"usually":[8],"missing":[9,22,51,98,149,154],"at":[10],"various":[11],"unexpected":[12],"moments":[13],"due":[14],"to":[15,48,87,105,121],"some":[16],"sensor":[17],"or":[18],"communication":[19],"errors.":[20],"These":[21],"values":[23,52,99],"do":[24],"not":[25],"only":[26],"influence":[27],"the":[28,50,77,92,123,126,157,160,173,176],"real-time":[29],"traffic":[30,36],"monitoring":[31],"but":[32],"also":[33],"prevent":[34],"further":[35],"data":[37],"mining.":[38],"this":[40],"paper,":[41],"we":[42],"propose":[43],"a":[44,107,111,116,152],"multi-view":[45],"learning":[46],"method":[47],"estimate":[49],"for":[53,147],"traffic-related":[54],"time":[55],"series":[56],"data.":[57,94],"The":[58,95,136],"model":[59,128,142],"combines":[60],"data-driven":[61],"algorithms":[62],"(long-short":[63],"term":[64],"memory":[65],"and":[66,70,79,84,134],"support":[67],"vector":[68],"regression)":[69],"collaborative":[71],"filtering":[72],"techniques.":[73],"It":[74],"can":[75,165,171],"consider":[76],"local":[78],"global":[80],"variation":[81],"in":[82,129],"temporal":[83],"spatial":[85],"views":[86,102,170],"capture":[88],"more":[89],"information":[90],"from":[91,100,115],"existing":[93],"estimations":[96],"four":[101],"aggregated":[104],"obtain":[106],"final":[108],"value":[109],"with":[110,151],"kernel":[112],"function.":[113],"Data":[114],"highway":[117],"network":[118],"used":[120],"evaluate":[122],"performance":[124,174],"proposed":[127,141],"terms":[130],"accuracy,":[132],"precision,":[133],"agreement.":[135],"results":[137],"indicate":[138],"that":[139,167],"our":[140],"outperforms":[143],"other":[144],"baselines,":[145],"especially":[146],"block":[148],"pattern":[150],"high":[153],"ratio.":[155],"Furthermore,":[156],"sensitivity":[158],"parameters":[161],"is":[162],"analyzed.":[163],"We":[164],"conclude":[166],"combining":[168],"different":[169],"improve":[172],"imputation.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":27},{"year":2024,"cited_by_count":33},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":34},{"year":2021,"cited_by_count":26},{"year":2020,"cited_by_count":33},{"year":2019,"cited_by_count":15},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}