{"id":"https://openalex.org/W2999130725","doi":"https://doi.org/10.1109/access.2020.2964644","title":"Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks","display_name":"Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W2999130725","doi":"https://doi.org/10.1109/access.2020.2964644","mag":"2999130725"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2964644","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2964644","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08951106.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08951106.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034851273","display_name":"Xiaozhou Liu","orcid":"https://orcid.org/0000-0002-2748-176X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaozhou Liu","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China","ORCiD"],"raw_orcid":"https://orcid.org/0000-0002-2748-176X","affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020229717","display_name":"Hengxing Cai","orcid":"https://orcid.org/0000-0001-9780-2330"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hengxing Cai","raw_affiliation_strings":["4Paradigm Inc., Beijing, China","ORCiD"],"raw_orcid":"https://orcid.org/0000-0001-9780-2330","affiliations":[{"raw_affiliation_string":"4Paradigm Inc., Beijing, China","institution_ids":[]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051786733","display_name":"Renxin Zhong","orcid":"https://orcid.org/0000-0003-1559-7287"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Renxin Zhong","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China","ORCiD"],"raw_orcid":"https://orcid.org/0000-0003-1559-7287","affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059153824","display_name":"Weili Sun","orcid":"https://orcid.org/0000-0002-0982-7771"},"institutions":[{"id":"https://openalex.org/I4210126311","display_name":"Beijing Transportation Research Center","ror":"https://ror.org/03pydk223","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210126311"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weili Sun","raw_affiliation_strings":["DiDi Smart Transportation, Beijing, China","ORCiD"],"raw_orcid":"https://orcid.org/0000-0002-0982-7771","affiliations":[{"raw_affiliation_string":"DiDi Smart Transportation, Beijing, China","institution_ids":["https://openalex.org/I4210126311"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003633670","display_name":"Junzhou Chen","orcid":"https://orcid.org/0000-0002-3388-3503"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junzhou Chen","raw_affiliation_strings":["Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China","ORCiD"],"raw_orcid":"https://orcid.org/0000-0002-3388-3503","affiliations":[{"raw_affiliation_string":"Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5034851273"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.6319,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.86961404,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"7916","last_page":"7924"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9983000159263611,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/computer-science","display_name":"Computer science","score":0.8092122077941895},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7439348697662354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5905308723449707},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37586307525634766},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3581649363040924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8092122077941895},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7439348697662354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5905308723449707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37586307525634766},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3581649363040924}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2964644","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2964644","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08951106.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:5a77b4634819474e9f82aa4fdd1581eb","is_oa":true,"landing_page_url":"https://doaj.org/article/5a77b4634819474e9f82aa4fdd1581eb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 7916-7924 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2964644","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2964644","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/08951106.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8796799315","display_name":null,"funder_award_id":"Grant U1811463","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2999130725.pdf","grobid_xml":"https://content.openalex.org/works/W2999130725.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W599626007","https://openalex.org/W633792684","https://openalex.org/W1522301498","https://openalex.org/W1570738611","https://openalex.org/W1580403931","https://openalex.org/W1596717185","https://openalex.org/W1665214252","https://openalex.org/W1904365287","https://openalex.org/W1966554111","https://openalex.org/W1975185440","https://openalex.org/W1986308189","https://openalex.org/W2055524127","https://openalex.org/W2061895067","https://openalex.org/W2069581686","https://openalex.org/W2087148973","https://openalex.org/W2095705004","https://openalex.org/W2101713460","https://openalex.org/W2114759709","https://openalex.org/W2123603662","https://openalex.org/W2135293965","https://openalex.org/W2163605009","https://openalex.org/W2246472911","https://openalex.org/W2249423915","https://openalex.org/W2249460372","https://openalex.org/W2260637115","https://openalex.org/W2508708614","https://openalex.org/W2579495707","https://openalex.org/W2755770938","https://openalex.org/W2764195553","https://openalex.org/W2768348081","https://openalex.org/W2790715082","https://openalex.org/W2806282362","https://openalex.org/W2807258775","https://openalex.org/W2808871417","https://openalex.org/W2898132656","https://openalex.org/W2911964244","https://openalex.org/W2945683996","https://openalex.org/W2947033505","https://openalex.org/W2963866024","https://openalex.org/W2972321833","https://openalex.org/W2979864289","https://openalex.org/W4230759094","https://openalex.org/W6618052993","https://openalex.org/W6620519365","https://openalex.org/W6631190155","https://openalex.org/W6634053702","https://openalex.org/W6634611111","https://openalex.org/W6637242042","https://openalex.org/W6640036494","https://openalex.org/W6655832309","https://openalex.org/W6674330103","https://openalex.org/W6675357634","https://openalex.org/W6678661989","https://openalex.org/W6684191040","https://openalex.org/W6691042019","https://openalex.org/W6691089252","https://openalex.org/W6691253256","https://openalex.org/W6745609711"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"The":[0,71,139],"timely":[1],"and":[2,15,76,103,167],"accurate":[3],"detection":[4,107,160],"of":[5,24,117],"traffic":[6,43,48,97,129],"incidents":[7,44],"is":[8,121],"beneficial":[9],"to":[10,40,68,83,126,152],"reduce":[11],"associated":[12],"economic":[13],"losses":[14],"avoid":[16],"secondary":[17],"crashes.":[18],"Inspired":[19],"by":[20,45,123],"the":[21,25,47,56,61,85,96,115,118,128,135,143,155,159,168,171],"impressive":[22],"success":[23],"image":[26],"classification":[27,112],"algorithms,":[28],"especially":[29],"convolutional":[30],"neural":[31],"networks":[32],"(CNNs),":[33],"this":[34],"study":[35],"proposes":[36],"a":[37,54,110,131],"novel":[38],"framework":[39,120],"detect":[41,127],"highway":[42],"learning":[46],"state":[49],"as":[50,109],"images.":[51,92],"In":[52],"such":[53],"framework,":[55],"probe":[57],"vehicles":[58],"equipped":[59],"with":[60,150,162],"global":[62],"positioning":[63],"system":[64],"equipment":[65],"are":[66,81],"used":[67,82],"obtain":[69],"data.":[70],"Gramian":[72],"Angular":[73],"Difference":[74],"Fields":[75],"Piecewise":[77],"Aggregation":[78],"Approximation":[79],"algorithms":[80,149],"convert":[84],"link":[86],"speed":[87],"time":[88],"series":[89],"data":[90],"into":[91],"CNNs":[93],"can":[94],"extract":[95],"features":[98],"based":[99],"on":[100],"these":[101],"images":[102],"consider":[104],"an":[105],"incident":[106],"problem":[108],"binary":[111],"task.":[113],"Further,":[114],"effectiveness":[116],"proposed":[119,144],"evaluated":[122],"applying":[124],"it":[125],"in":[130],"real-world":[132],"environment,":[133],"i.e.,":[134],"Guangzhou":[136],"Airport":[137],"Expressway.":[138],"results":[140],"illustrate":[141],"that":[142],"model":[145],"outperforms":[146],"several":[147],"other":[148],"respect":[151],"almost":[153],"all":[154],"performance":[156],"indexes,":[157],"including":[158],"rate":[161],"different":[163],"false":[164],"alarm":[165],"rates":[166],"area":[169],"under":[170],"receiver":[172],"operating":[173],"characteristic":[174],"curve.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-05-12T08:28:47.272897","created_date":"2025-10-10T00:00:00"}