{"id":"https://openalex.org/W2888797051","doi":"https://doi.org/10.1109/tits.2018.2864612","title":"Multi-Dimensional Traffic Congestion Detection Based on Fusion of Visual Features and Convolutional Neural Network","display_name":"Multi-Dimensional Traffic Congestion Detection Based on Fusion of Visual Features and Convolutional Neural Network","publication_year":2018,"publication_date":"2018-08-30","ids":{"openalex":"https://openalex.org/W2888797051","doi":"https://doi.org/10.1109/tits.2018.2864612","mag":"2888797051"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2018.2864612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2864612","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on 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/A5101498868","display_name":"Xiao Ke","orcid":"https://orcid.org/0000-0001-9059-5391"},"institutions":[{"id":"https://openalex.org/I1327237609","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934","country_code":"CN","type":"government","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Ke","raw_affiliation_strings":["Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9059-5391","affiliations":[{"raw_affiliation_string":"Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou, China","institution_ids":["https://openalex.org/I1327237609"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102746507","display_name":"Ling-Feng Shi","orcid":"https://orcid.org/0000-0003-3795-0440"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingfeng Shi","raw_affiliation_strings":["Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3795-0440","affiliations":[{"raw_affiliation_string":"Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100643723","display_name":"Wenzhong Guo","orcid":"https://orcid.org/0000-0003-4118-8823"},"institutions":[{"id":"https://openalex.org/I1327237609","display_name":"Ministry of Education of the People's Republic of China","ror":"https://ror.org/01mv9t934","country_code":"CN","type":"government","lineage":["https://openalex.org/I1327237609","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhong Guo","raw_affiliation_strings":["Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4118-8823","affiliations":[{"raw_affiliation_string":"Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou, China","institution_ids":["https://openalex.org/I1327237609"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061331291","display_name":"Dewang Chen","orcid":"https://orcid.org/0000-0002-8660-9700"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dewang Chen","raw_affiliation_strings":["Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8660-9700","affiliations":[{"raw_affiliation_string":"Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.786,"has_fulltext":false,"cited_by_count":88,"citation_normalized_percentile":{"value":0.96460226,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"20","issue":"6","first_page":"2157","last_page":"2170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9961000084877014,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9886000156402588,"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/computer-science","display_name":"Computer science","score":0.7256661057472229},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6986216902732849},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5983057618141174},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5978784561157227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5705708265304565},{"id":"https://openalex.org/keywords/traffic-congestion-reconstruction-with-kerners-three-phase-theory","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","score":0.5365837216377258},{"id":"https://openalex.org/keywords/floating-car-data","display_name":"Floating car data","score":0.5314192771911621},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.47817593812942505},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4502153992652893},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4445287585258484},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.4434293210506439},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4364827275276184},{"id":"https://openalex.org/keywords/foreground-detection","display_name":"Foreground detection","score":0.4364287555217743},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4361700415611267},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4336732029914856},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41865792870521545},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3992115259170532},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.23118358850479126},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1876550316810608},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13791140913963318},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.08444133400917053}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7256661057472229},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6986216902732849},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5983057618141174},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5978784561157227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5705708265304565},{"id":"https://openalex.org/C25492975","wikidata":"https://www.wikidata.org/wiki/Q960570","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","level":3,"score":0.5365837216377258},{"id":"https://openalex.org/C64093975","wikidata":"https://www.wikidata.org/wiki/Q356677","display_name":"Floating car data","level":3,"score":0.5314192771911621},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.47817593812942505},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4502153992652893},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4445287585258484},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.4434293210506439},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4364827275276184},{"id":"https://openalex.org/C2779769447","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Foreground detection","level":4,"score":0.4364287555217743},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4361700415611267},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4336732029914856},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41865792870521545},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3992115259170532},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.23118358850479126},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1876550316810608},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13791140913963318},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.08444133400917053},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2018.2864612","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2864612","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.49000000953674316}],"awards":[{"id":"https://openalex.org/G1321117299","display_name":"\u7fa4\u667a\u611f\u77e5\u8f66\u8054\u7f51\u7684\u9ad8\u6548\u6570\u636e\u878d\u5408\u4e0e\u4ea4\u901a\u6d41\u9884\u6d4b\u7814\u7a76","funder_award_id":"61672159","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3791469392","display_name":null,"funder_award_id":"61502105","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G725298673","display_name":"\u4e0e\u4e3b\u89c2\u611f\u77e5\u4e00\u81f4\u7684\u7acb\u4f53\u56fe\u50cf\u548c\u89c6\u9891\u89c6\u89c9\u8d28\u91cf\u5ba2\u89c2\u8bc4\u4ef7\u4e0e\u589e\u5f3a\u7814\u7a76","funder_award_id":"61672158","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":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1972314457","https://openalex.org/W1980508392","https://openalex.org/W1987236203","https://openalex.org/W1990896158","https://openalex.org/W1994623790","https://openalex.org/W2009234019","https://openalex.org/W2025025927","https://openalex.org/W2027797775","https://openalex.org/W2042956327","https://openalex.org/W2055190781","https://openalex.org/W2060766460","https://openalex.org/W2062299643","https://openalex.org/W2070439088","https://openalex.org/W2074199334","https://openalex.org/W2091814216","https://openalex.org/W2102625004","https://openalex.org/W2105184687","https://openalex.org/W2106839910","https://openalex.org/W2118579359","https://openalex.org/W2118877769","https://openalex.org/W2121255121","https://openalex.org/W2127070222","https://openalex.org/W2132461991","https://openalex.org/W2138459266","https://openalex.org/W2166619090","https://openalex.org/W2331471580","https://openalex.org/W2465108587","https://openalex.org/W2473640056","https://openalex.org/W2519108195","https://openalex.org/W2530263920","https://openalex.org/W2550363511","https://openalex.org/W2563755014","https://openalex.org/W4302441299","https://openalex.org/W6647200086","https://openalex.org/W6648000902","https://openalex.org/W6677548441","https://openalex.org/W6679388247"],"related_works":["https://openalex.org/W1977648593","https://openalex.org/W2348780717","https://openalex.org/W2780165746","https://openalex.org/W2602033589","https://openalex.org/W4235475963","https://openalex.org/W4252030899","https://openalex.org/W2374077716","https://openalex.org/W1993364152","https://openalex.org/W3087821047","https://openalex.org/W1988111721"],"abstract_inverted_index":{"In":[0],"intelligent":[1],"transportation":[2],"systems,":[3],"there":[4],"are":[5,32],"many":[6],"tasks":[7],"that":[8,54,182],"rely":[9],"on":[10,57],"the":[11,42,71,83,92,108,111,119,123,127,166,183,191,199,205],"detection":[12,31,53,133,195],"of":[13,73,85,136,161,170,201],"road":[14,43,51,131,146,171],"congestion,":[15],"such":[16],"as":[17],"traffic":[18,22,29,142,144,149],"signal":[19],"scheduling":[20],"and":[21,37,61,110,148,168,178],"accident":[23],"detection.":[24,173],"As":[25],"traditional":[26],"methods":[27,196],"for":[28,50],"congestion":[30,52,132,172,194],"difficult":[33],"to":[34,41,106,116,164,188,198],"use,":[35],"expensive,":[36],"may":[38],"cause":[39],"damage":[40],"surface,":[44],"this":[45],"paper":[46],"presents":[47],"a":[48,62,78,100,137,159],"method":[49,68,129,157,185],"is":[55,88,104,113,186],"based":[56],"multidimensional":[58,138,202],"visual":[59],"features":[60,203],"convolutional":[63],"neural":[64],"network":[65],"(CNN).":[66],"This":[67],"first":[69],"detects":[70],"density":[72],"foreground":[74,121],"objects":[75,87],"by":[76,90],"using":[77,91,158,204],"gray-level":[79],"co-occurrence":[80],"matrix;":[81],"second,":[82],"speed":[84],"moving":[86],"detected":[89],"Lucas-Kanade":[93],"optical":[94,162],"flow":[95,163],"with":[96],"pyramid":[97],"implementation.":[98],"Third,":[99],"Gaussian":[101],"mixture":[102],"model":[103,107],"used":[105,115],"background,":[109],"CNN":[112],"then":[114],"accurately":[117],"detect":[118],"final":[120],"from":[122],"candidate":[124],"foregrounds.":[125],"Finally,":[126],"proposed":[128,184],"performs":[130],"in":[134],"terms":[135],"feature":[139],"space,":[140],"including":[141],"density,":[143],"velocity,":[145],"occupancy,":[147],"flow.":[150],"Furthermore,":[151],"we":[152],"propose":[153],"an":[154],"information":[155],"entropy":[156],"histogram":[160],"enhance":[165],"accuracy":[167],"reliability":[169],"Simulation":[174],"results":[175],"via":[176],"quantitative":[177],"qualitative":[179],"assessment":[180],"indicate":[181],"able":[187],"significantly":[189],"outperform":[190],"state-of-the-art":[192],"road-traffic":[193],"due":[197],"fusion":[200],"CNN.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":19},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":9}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}