{"id":"https://openalex.org/W3017019192","doi":"https://doi.org/10.1080/15472450.2020.1742121","title":"Convolutional neural network for recognizing highway traffic congestion","display_name":"Convolutional neural network for recognizing highway traffic congestion","publication_year":2020,"publication_date":"2020-04-13","ids":{"openalex":"https://openalex.org/W3017019192","doi":"https://doi.org/10.1080/15472450.2020.1742121","mag":"3017019192"},"language":"en","primary_location":{"id":"doi:10.1080/15472450.2020.1742121","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2020.1742121","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/A5068084215","display_name":"Hua Cui","orcid":"https://orcid.org/0000-0003-4769-9464"},"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":"Hua Cui","raw_affiliation_strings":["Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107953714","display_name":"Ge\u2010Ge Yuan","orcid":null},"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":"Gege Yuan","raw_affiliation_strings":["Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100647137","display_name":"Geoffrey Liu","orcid":"https://orcid.org/0000-0002-2603-7296"},"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":true,"raw_author_name":"Ni Liu","raw_affiliation_strings":["Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016050256","display_name":"Mingyuan Xu","orcid":"https://orcid.org/0000-0003-0463-4249"},"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":"Mingyuan Xu","raw_affiliation_strings":["Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008672520","display_name":"Huansheng Song","orcid":"https://orcid.org/0000-0002-8590-0061"},"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":"Huansheng Song","raw_affiliation_strings":["Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Informationw Engineetring, Chang\u2019an University, Xi\u2019an, China","institution_ids":["https://openalex.org/I25355098"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100647137"],"corresponding_institution_ids":["https://openalex.org/I25355098"],"apc_list":null,"apc_paid":null,"fwci":4.4377,"has_fulltext":false,"cited_by_count":59,"citation_normalized_percentile":{"value":0.94587355,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"24","issue":"3","first_page":"279","last_page":"289"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9987999796867371,"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":0.9987999796867371,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.9907000064849854,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8738678097724915},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7269070744514465},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6209359169006348},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.554336428642273},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5165404677391052},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5069141983985901},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.500838041305542},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4891829788684845},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4443092942237854},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.41844138503074646},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40140479803085327},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.15639358758926392},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08539292216300964}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8738678097724915},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7269070744514465},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6209359169006348},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.554336428642273},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5165404677391052},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5069141983985901},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.500838041305542},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4891829788684845},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4443092942237854},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.41844138503074646},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40140479803085327},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.15639358758926392},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08539292216300964},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/15472450.2020.1742121","is_oa":false,"landing_page_url":"https://doi.org/10.1080/15472450.2020.1742121","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":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W74974833","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1966330783","https://openalex.org/W2005409769","https://openalex.org/W2033366399","https://openalex.org/W2035299451","https://openalex.org/W2086770037","https://openalex.org/W2097117768","https://openalex.org/W2097144242","https://openalex.org/W2108598243","https://openalex.org/W2118942996","https://openalex.org/W2126918828","https://openalex.org/W2144913102","https://openalex.org/W2145983039","https://openalex.org/W2155994925","https://openalex.org/W2156029399","https://openalex.org/W2163605009","https://openalex.org/W2255467824","https://openalex.org/W2294503962","https://openalex.org/W2506082243","https://openalex.org/W2507237164","https://openalex.org/W2570804084","https://openalex.org/W2600889608","https://openalex.org/W2896674202","https://openalex.org/W2897741166","https://openalex.org/W2906093258","https://openalex.org/W2944892868","https://openalex.org/W2945006873","https://openalex.org/W2946281080","https://openalex.org/W2950214697","https://openalex.org/W2951774052","https://openalex.org/W2953106684","https://openalex.org/W2963374347","https://openalex.org/W2981611875"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2949096641","https://openalex.org/W2970686063"],"abstract_inverted_index":{"We":[0,86,127],"investigates":[1],"the":[2,22,48,139,154,167,179,207,227],"performance":[3],"of":[4,39,45,73,82,111,149,153,166,182,198,206],"deep":[5],"Convolutional":[6],"Neural":[7],"Network":[8],"(CNN)":[9],"for":[10],"recognizing":[11],"highway":[12,30,74,130],"traffic":[13,135],"congestion":[14,125,170],"state":[15],"in":[16,25,60,223],"surveillance":[17,31],"camera":[18],"images.":[19,62],"Different":[20],"from":[21,67],"usual":[23],"images":[24,27,66,144,184,209],"ImageNet,":[26],"generated":[28],"by":[29],"cameras":[32,68],"usually":[33],"have":[34,161],"much":[35],"more":[36],"extensive":[37],"range":[38,148],"perspective":[40,222],"and":[41,51,119,157,160,190,221],"thus":[42],"larger":[43],"area":[44],"background.":[46],"Therefore":[47],"objective":[49,112],"road":[50,84,150],"vehicles":[52],"are":[53,87,95,121],"not":[54],"as":[55,57,109],"prominent":[56],"target":[58],"object":[59],"ImageNet":[61],"And":[63,204],"also":[64],"these":[65],"across":[69],"a":[70,78,129,146],"large":[71],"number":[72],"sites":[75],"could":[76,225],"show":[77],"very":[79,88],"rich":[80],"variance":[81],"scenes,":[83],"configurations.":[85],"interested":[89],"to":[90,99,123,137,212],"study":[91],"whether":[92],"convolutional":[93,116],"networks":[94],"still":[96],"reliably":[97],"able":[98],"classify":[100,124],"such":[101,108],"images,":[102],"without":[103],"any":[104],"special":[105],"previous":[106],"processing":[107],"segmentation":[110],"roads.":[113],"Two":[114],"classic":[115],"networks,":[117,187],"AlexNet":[118,189],"GoogLeNet":[120,191],"employed":[122],"state.":[126],"build":[128],"imagery":[131],"dataset":[132],"using":[133],"real-life":[134],"videos":[136],"evaluate":[138],"CNNs":[140],"recognition":[141,196,228],"performance.":[142],"These":[143],"cover":[145],"wide":[147],"configurations,":[151],"times":[152],"day,":[155],"weather":[156],"lighting":[158],"conditions,":[159],"been":[162],"labeled":[163],"with":[164],"one":[165],"two":[168],"states,":[169],"or":[171],"non-congestion.":[172],"The":[173],"experimental":[174],"results":[175,217],"indicate":[176],"that":[177,219],"under":[178],"current":[180],"strategy":[181],"feeding":[183],"directly":[185],"into":[186],"both":[188],"can":[192],"achieve":[193],"an":[194],"excellent":[195],"accuracy":[197],"98%":[199],"on":[200],"held-out":[201],"test":[202],"samples.":[203],"many":[205],"misclassified":[208],"turn":[210],"out":[211],"be":[213],"borderline":[214],"cases.":[215],"More":[216],"include":[218],"scale":[220],"photography":[224],"affect":[226],"result.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}