{"id":"https://openalex.org/W2791112721","doi":"https://doi.org/10.1080/15472450.2018.1441027","title":"Comparison of vehicle re-identification models for trucks based on axle spacing measurements","display_name":"Comparison of vehicle re-identification models for trucks based on axle spacing measurements","publication_year":2018,"publication_date":"2018-02-15","ids":{"openalex":"https://openalex.org/W2791112721","doi":"https://doi.org/10.1080/15472450.2018.1441027","mag":"2791112721"},"language":"en","primary_location":{"id":"doi:10.1080/15472450.2018.1441027","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2018.1441027","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of 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/A5037353799","display_name":"Gulsevi Basar","orcid":"https://orcid.org/0000-0003-2912-7008"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gulsevi Basar","raw_affiliation_strings":["Modeling, Simulation, and Visualization Engineering Department, Old Dominion University, Norfolk, Virginia, USA"],"affiliations":[{"raw_affiliation_string":"Modeling, Simulation, and Visualization Engineering Department, Old Dominion University, Norfolk, Virginia, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064291992","display_name":"Mecit Cetin","orcid":"https://orcid.org/0000-0003-2003-9343"},"institutions":[{"id":"https://openalex.org/I81365321","display_name":"Old Dominion University","ror":"https://ror.org/04zjtrb98","country_code":"US","type":"education","lineage":["https://openalex.org/I81365321"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mecit Cetin","raw_affiliation_strings":["Civil &amp; Environmental Engineering Department, Old Dominion University, Norfolk, Virginia, USA"],"affiliations":[{"raw_affiliation_string":"Civil &amp; Environmental Engineering Department, Old Dominion University, Norfolk, Virginia, USA","institution_ids":["https://openalex.org/I81365321"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001195045","display_name":"Andrew P. Nichols","orcid":"https://orcid.org/0000-0003-1760-2393"},"institutions":[{"id":"https://openalex.org/I88694374","display_name":"Marshall University","ror":"https://ror.org/02erqft81","country_code":"US","type":"education","lineage":["https://openalex.org/I88694374"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew P. Nichols","raw_affiliation_strings":["Weisberg Division of Engineering, Marshall University, Huntington, West Virginia, USA"],"affiliations":[{"raw_affiliation_string":"Weisberg Division of Engineering, Marshall University, Huntington, West Virginia, USA","institution_ids":["https://openalex.org/I88694374"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5037353799"],"corresponding_institution_ids":["https://openalex.org/I81365321"],"apc_list":null,"apc_paid":null,"fwci":0.6835,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.67626771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"22","issue":"6","first_page":"517","last_page":"529"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14304","display_name":"Transport Systems and Technology","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"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/T14304","display_name":"Transport Systems and Technology","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9860000014305115,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9825999736785889,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6700280904769897},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.6365649700164795},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6249690055847168},{"id":"https://openalex.org/keywords/truck","display_name":"Truck","score":0.6117023229598999},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5725659132003784},{"id":"https://openalex.org/keywords/axle","display_name":"Axle","score":0.5317348837852478},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5240852236747742},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.4669818580150604},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45761245489120483},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4484071731567383},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.43171870708465576},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4027710258960724},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35792601108551025},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3381063640117645},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33650290966033936},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3191908299922943},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22687193751335144},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.10887038707733154},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.09456184506416321}],"concepts":[{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6700280904769897},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6365649700164795},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6249690055847168},{"id":"https://openalex.org/C52121051","wikidata":"https://www.wikidata.org/wiki/Q43193","display_name":"Truck","level":2,"score":0.6117023229598999},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5725659132003784},{"id":"https://openalex.org/C129727815","wikidata":"https://www.wikidata.org/wiki/Q188209","display_name":"Axle","level":2,"score":0.5317348837852478},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5240852236747742},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.4669818580150604},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45761245489120483},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4484071731567383},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.43171870708465576},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4027710258960724},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35792601108551025},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3381063640117645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33650290966033936},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3191908299922943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22687193751335144},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.10887038707733154},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.09456184506416321},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/15472450.2018.1441027","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2018.1441027","pdf_url":null,"source":{"id":"https://openalex.org/S172631016","display_name":"Journal of Intelligent Transportation Systems","issn_l":"1547-2442","issn":["1547-2442","1547-2450"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309493","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02"},{"id":"https://openalex.org/F4320310578","display_name":"University of Maryland","ror":"https://ror.org/01r0c1p88"},{"id":"https://openalex.org/F4320315300","display_name":"Oregon Department of Transportation","ror":null},{"id":"https://openalex.org/F4320332393","display_name":"Federal Highway Administration","ror":"https://ror.org/0473rr271"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W563996775","https://openalex.org/W653395669","https://openalex.org/W1545005344","https://openalex.org/W1801413419","https://openalex.org/W1968765912","https://openalex.org/W1970358178","https://openalex.org/W1984533653","https://openalex.org/W1985191478","https://openalex.org/W1991052563","https://openalex.org/W1994116880","https://openalex.org/W2004051418","https://openalex.org/W2007099606","https://openalex.org/W2008889836","https://openalex.org/W2011441107","https://openalex.org/W2025653611","https://openalex.org/W2029645832","https://openalex.org/W2029748634","https://openalex.org/W2072260982","https://openalex.org/W2077916829","https://openalex.org/W2086728115","https://openalex.org/W2093687484","https://openalex.org/W2102067281","https://openalex.org/W2109006789","https://openalex.org/W2111789455","https://openalex.org/W2118558728","https://openalex.org/W2131934180","https://openalex.org/W2132780053","https://openalex.org/W2141035746","https://openalex.org/W2157428127","https://openalex.org/W2170631965","https://openalex.org/W2513554586","https://openalex.org/W2582743722","https://openalex.org/W2589592971"],"related_works":["https://openalex.org/W1517019597","https://openalex.org/W1968776045","https://openalex.org/W650759427","https://openalex.org/W2296713838","https://openalex.org/W767149399","https://openalex.org/W27335093","https://openalex.org/W2134091373","https://openalex.org/W626161498","https://openalex.org/W4234504473","https://openalex.org/W3009154171"],"abstract_inverted_index":{"In":[0,61,97,165],"previous":[1],"research,":[2],"it":[3],"has":[4],"been":[5],"demonstrated":[6],"that":[7,125],"there":[8],"is":[9,106,179],"enough":[10],"variation":[11],"within":[12],"the":[13,69,86,89,99,127,146,151,168,187,202],"truck":[14],"population":[15],"in":[16],"terms":[17],"of":[18,50,55,88,101,118,150,161,190,205,221],"axle":[19],"spacings":[20],"and":[21,53,58,85,208,213],"vehicle":[22,27],"lengths,":[23],"which":[24],"enable":[25],"anonymous":[26],"re-identification":[28,70,90,128,206],"between":[29,40,170],"two":[30,34,41,173],"measurement":[31],"stations":[32,174],"(e.g.,":[33],"weigh-in-motion":[35],"(WIM)":[36],"sites).":[37],"Matching":[38],"trucks":[39],"sites":[42],"can":[43,198],"support":[44],"various":[45],"applications,":[46],"such":[47],"as":[48,92,175],"calibration":[49],"WIM":[51,120,191],"equipment":[52],"estimation":[54],"travel":[56],"times":[57],"origin-destination":[59],"flows.":[60],"this":[62],"paper,":[63],"several":[64],"modeling":[65],"approaches":[66],"to":[67,181,194],"solve":[68],"problem":[71,91,129],"are":[72,143,163,211,223],"explored":[73],"including":[74],"Na\u00efve":[75],"Bayes":[76,229],"(NB),":[77],"Bayesian":[78],"Models":[79],"(BM)":[80],"fitted":[81],"by":[82,133,145],"mixture":[83,134],"models,":[84,140],"formulation":[87,149],"a":[93,103,158,176,218],"mathematical":[94,147],"assignment":[95,148],"problem.":[96],"addition,":[98,166],"influence":[100],"selecting":[102],"similarity":[104,162,169,209,222],"measure":[105,210],"evaluated":[107],"through":[108],"numerical":[109],"experiments":[110],"conducted":[111],"on":[112],"real-world":[113],"data":[114],"from":[115,172],"six":[116],"pairs":[117,189],"upstream\u2013downstream":[119],"stations.":[121],"The":[122],"results":[123],"demonstrate":[124],"solving":[126,137],"with":[130,138],"BMs":[131],"fit":[132],"distributions":[135],"outperforms":[136],"NB":[139],"while":[141],"both":[142],"outperformed":[144],"same":[152],"problem,":[153],"especially":[154],"when":[155,201],"vehicle-pairs":[156,216],"exceeding":[157,217],"high":[159,219],"threshold":[160,220],"matched.":[164],"expressing":[167],"measurements":[171],"percentage":[177],"difference":[178],"found":[180],"be":[182,199],"relatively":[183],"more":[184],"advantageous.":[185],"For":[186],"presented":[188],"stations,":[192],"up":[193],"90%":[195],"matching":[196],"accuracy":[197],"achieved":[200],"best":[203],"combination":[204],"method":[207],"implemented,":[212],"only":[214],"those":[215],"matched.AbbreviationsAAAssignment":[224],"AlgorithmBMBayesian":[225],"ModelDDownstreamGMMGaussian":[226],"Mixture":[227],"ModelNBNa\u00efve":[228],"MethodpdfProbability":[230],"Density":[231],"FunctionUUpstreamVRIVehicle":[232],"Re-Identification":[233]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
