{"id":"https://openalex.org/W147200611","doi":null,"title":"Correction of Erroneous Vehicle Speed Data with Locally Weighted Regression for GPS-Based Probe Vehicle Monitoring","display_name":"Correction of Erroneous Vehicle Speed Data with Locally Weighted Regression for GPS-Based Probe Vehicle Monitoring","publication_year":2011,"publication_date":"2011-01-01","ids":{"openalex":"https://openalex.org/W147200611","mag":"147200611"},"language":"en","primary_location":{"id":"mag:147200611","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/1091275","pdf_url":null,"source":{"id":"https://openalex.org/S4306533228","display_name":"Transportation Research Board 90th Annual MeetingTransportation Research Board","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":"Transportation Research Board 90th Annual MeetingTransportation Research Board","raw_type":null},"volume":null,"type":"erratum","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/A5053519107","display_name":"Junhyeong Park","orcid":"https://orcid.org/0000-0002-7361-1334","observed_orcids":["https://orcid.org/0000-0002-7361-1334"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Junhyeong Park","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-7361-1334","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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":0.8730000257492065,"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":0.8730000257492065,"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":0.09759999811649323,"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/T14304","display_name":"Transport Systems and Technology","score":0.003599999938160181,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/vehicle-speed-data","display_name":"vehicle speed data","score":0.7670000195503235},{"id":"https://openalex.org/keywords/outlier-detection","display_name":"outlier detection","score":0.8740000128746033},{"id":"https://openalex.org/keywords/outlier-correction","display_name":"outlier correction","score":0.8309999704360962},{"id":"https://openalex.org/keywords/locally-weighted-regression","display_name":"locally weighted regression","score":0.9750000238418579},{"id":"https://openalex.org/keywords/traffic-data-quality","display_name":"traffic data quality","score":0.7519999742507935},{"id":"https://openalex.org/keywords/smoothing","display_name":"smoothing","score":0.5210000276565552},{"id":"https://openalex.org/keywords/exponential-smoothing","display_name":"exponential smoothing","score":0.8899999856948853},{"id":"https://openalex.org/keywords/traffic-monitoring","display_name":"traffic monitoring","score":0.6420000195503235}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7921473979949951},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.7443211078643799},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.6776682138442993},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6758987307548523},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6254284977912903},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5677105188369751},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.5667712688446045},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4515650272369385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24659785628318787},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.06977084279060364},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:147200611","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/1091275","pdf_url":null,"source":{"id":"https://openalex.org/S4306533228","display_name":"Transportation Research Board 90th Annual MeetingTransportation Research Board","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":"Transportation Research Board 90th Annual MeetingTransportation Research Board","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5142999887466431,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"},{"score":0.4943999946117401,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7039431936","https://openalex.org/W1523106795","https://openalex.org/W2249999447","https://openalex.org/W350015000","https://openalex.org/W3215251621","https://openalex.org/W2008406490","https://openalex.org/W2247318224","https://openalex.org/W3087641844","https://openalex.org/W4391823355","https://openalex.org/W3179246218"],"abstract_inverted_index":{"Effective":[0],"detection":[1],"and":[2,53,79,111,151],"correction":[3],"of":[4,15,28,41,121,141],"outliers":[5,78,85],"in":[6,132,166],"raw":[7,29],"traffic":[8,20,36,51],"data":[9,117],"collected":[10],"from":[11],"the":[12,26,92,102,106,127,139,142,157,162],"field":[13],"is":[14,22,109,130],"keen":[16],"interest":[17],"because":[18],"reliable":[19,116],"information":[21,54],"highly":[23],"dependent":[24],"on":[25],"quality":[27],"data.":[30,71,137],"Global":[31],"positioning":[32],"systems":[33,38],"(GPS)":[34],"based":[35,64],"surveillance":[37],"are":[39,47,86],"capable":[40],"producing":[42],"individual":[43,68,134],"vehicle":[44,69,135],"speeds":[45],"that":[46,104,126,156],"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":[65,133],"method":[66,96,129,148,160,165],"speed":[70,136],"Both":[72],"technique":[74],"to":[75,82,89,97,113],"generate":[76],"synthetic":[77,84],"two":[80],"approaches":[81],"injecting":[83],"also":[87],"presented":[88],"systematically":[90],"evaluate":[91],"proposed":[93,128,158],"method.":[94],"A":[95,119],"determine":[98],"parameters":[99],"associated":[100],"with":[101],"LWR":[103],"affect":[105],"smoothing":[107,147,164],"performance":[108,143,167],"devised":[110],"applied":[112],"obtain":[114],"more":[115],"correction.":[118],"set":[120],"illustrative":[122],"evaluation":[123],"examples":[124],"explains":[125],"useful":[131],"For":[138],"purpose":[140],"comparison,":[144],"an":[145],"exponential":[146,163],"was":[149,154],"adopted":[150],"evaluated.":[152],"It":[153],"identified":[155],"LWR-based":[159],"outperformed":[161],"evaluations.":[168]},"counts_by_year":[],"updated_date":"2026-09-22T06:50:58.402377","created_date":"2016-06-24T00:00:00"}