{"id":"https://openalex.org/W18017258","doi":null,"title":"Methodology for Correcting Erroneous Individual Vehicle Speed Data Using Locally Weighted Regression","display_name":"Methodology for Correcting Erroneous Individual Vehicle Speed Data Using Locally Weighted Regression","publication_year":2010,"publication_date":"2010-01-01","ids":{"openalex":"https://openalex.org/W18017258","mag":"18017258"},"language":"en","primary_location":{"id":"mag:18017258","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/1134993","pdf_url":null,"source":{"id":"https://openalex.org/S4306497379","display_name":"17th ITS World CongressITS JapanITS AmericaERTICO","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"17th ITS World CongressITS JapanITS AmericaERTICO","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5059236183","display_name":"Heesub Rim","orcid":"https://orcid.org/0000-0001-6059-5155","observed_orcids":["https://orcid.org/0000-0001-6059-5155"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heesub Rim","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-6059-5155","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071250466","display_name":"Cheol H. Oh","orcid":"https://orcid.org/0000-0002-1431-9480","observed_orcids":["https://orcid.org/0000-0002-1431-9480"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheol Oh","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-1431-9480","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056898095","display_name":"Junhyung Park","orcid":"https://orcid.org/0000-0002-0805-6362","observed_orcids":["https://orcid.org/0000-0002-0805-6362"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junhyung Park","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0805-6362","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5046136185","display_name":"Gunwoo Lee","orcid":"https://orcid.org/0000-0002-5159-3948","observed_orcids":["https://orcid.org/0000-0002-5159-3948"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gunwoo Lee","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-5159-3948","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00397088,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8374000191688538,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8374000191688538,"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/T10524","display_name":"Traffic control and management","score":0.09880000352859497,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.008500000461935997,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier-detection","display_name":"outlier detection","score":0.7360000014305115},{"id":"https://openalex.org/keywords/outlier-correction","display_name":"outlier correction","score":0.8820000290870667},{"id":"https://openalex.org/keywords/locally-weighted-regression","display_name":"locally weighted regression","score":0.9829999804496765},{"id":"https://openalex.org/keywords/data-smoothing","display_name":"data smoothing","score":0.5799999833106995},{"id":"https://openalex.org/keywords/bandwidth-selection","display_name":"bandwidth selection","score":0.7910000085830688},{"id":"https://openalex.org/keywords/traffic-data-quality","display_name":"traffic data quality","score":0.9010000228881836}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8236978054046631},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6799852848052979},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.6662518978118896},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6404551267623901},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6160869002342224},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5583335757255554},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.48081839084625244},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4496781826019287},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4479779005050659},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4124619960784912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3159486651420593},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18259480595588684},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13891848921775818},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08961117267608643},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:18017258","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/1134993","pdf_url":null,"source":{"id":"https://openalex.org/S4306497379","display_name":"17th ITS World CongressITS JapanITS AmericaERTICO","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"17th ITS World CongressITS JapanITS AmericaERTICO","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.47999998927116394,"display_name":"Sustainable cities and communities"},{"id":"https://metadata.un.org/sdg/9","score":0.428600013256073,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1523106795","https://openalex.org/W2249999447","https://openalex.org/W2008406490","https://openalex.org/W2247318224","https://openalex.org/W3215251621","https://openalex.org/W4391823355","https://openalex.org/W350015000","https://openalex.org/W3087641844","https://openalex.org/W4417053567","https://openalex.org/W3179246218"],"abstract_inverted_index":{"Effective":[0],"detection":[1],"and":[2,53,79,109],"correction":[3],"of":[4,15,28,41,119],"outliers":[5,78,83],"in":[6,130],"raw":[7,29],"traffic":[8,20,36,51],"data":[9,115],"collected":[10],"from":[11],"the":[12,26,90,100,104,125],"field":[13],"is":[14,22,107,128],"keen":[16],"interest":[17],"because":[18],"reliable":[19,114],"information":[21,54],"highly":[23],"dependent":[24],"on":[25],"quality":[27],"data.":[30,71,135],"Global":[31],"Positioning":[32],"System":[33],"(GPS)":[34],"based":[35,64],"surveillance":[37],"systems":[38],"are":[39,47,84],"capable":[40],"producing":[42],"individual":[43,68,132],"vehicle":[44,69,133],"speeds":[45],"that":[46,102,124],"invaluable":[48],"for":[49,67],"various":[50],"management":[52],"strategies.":[55],"This":[56],"study":[57],"proposes":[58],"a":[59,73],"locally":[60],"weighted":[61],"regression":[62],"(LWR)":[63],"filtering":[65,131],"method":[66,94,127],"speed":[70,134],"Both":[72],"technique":[74],"to":[75,80,87,95,111],"generate":[76],"synthetic":[77,82],"injecting":[81],"also":[85],"presented":[86],"systematically":[88],"evaluate":[89],"proposed":[91,126],"method.":[92],"A":[93,117],"determine":[96],"parameters":[97],"associated":[98],"with":[99],"LWR":[101],"affect":[103],"smoothing":[105],"performance":[106],"devised":[108],"applied":[110],"obtain":[112],"more":[113],"correction.":[116],"set":[118],"illustrative":[120],"evaluation":[121],"examples":[122],"explains":[123],"useful":[129]},"counts_by_year":[],"updated_date":"2026-09-22T06:50:58.402377","created_date":"2016-06-24T00:00:00"}
