{"id":"https://openalex.org/W3137297791","doi":"https://doi.org/10.1109/bigdata50022.2020.9378464","title":"Fusion-3DCNN-max3P: A dynamic system for discovering patterns of predicted congestion","display_name":"Fusion-3DCNN-max3P: A dynamic system for discovering patterns of predicted congestion","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3137297791","doi":"https://doi.org/10.1109/bigdata50022.2020.9378464","mag":"3137297791"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5023083273","display_name":"Minh-Son Dao","orcid":"https://orcid.org/0000-0003-3044-8175"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Minh-Son Dao","raw_affiliation_strings":["National Institute of Information and Communications Technology,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology,Tokyo,Japan","institution_ids":["https://openalex.org/I90023481"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047517734","display_name":"Ngoc Thanh Nguy\u00ean","orcid":"https://orcid.org/0000-0002-3247-2948"},"institutions":[{"id":"https://openalex.org/I123565023","display_name":"Vietnam National University Ho Chi Minh City","ror":"https://ror.org/00waaqh38","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Ngoc-Thanh Nguyen","raw_affiliation_strings":["University of Information Technology Vietnam National University - HCM city,HCM city,Vietnam"],"affiliations":[{"raw_affiliation_string":"University of Information Technology Vietnam National University - HCM city,HCM city,Vietnam","institution_ids":["https://openalex.org/I123565023"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064495970","display_name":"R. Uday Kiran","orcid":"https://orcid.org/0000-0002-5417-0289"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]},{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"R. Uday Kiran","raw_affiliation_strings":["The University of Aizu, Japan NICT, Tokyo, Japan The University of Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The University of Aizu, Japan NICT, Tokyo, Japan The University of Tokyo,Japan","institution_ids":["https://openalex.org/I141591182","https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048072689","display_name":"Koji Zettsu","orcid":"https://orcid.org/0000-0003-4062-2376"},"institutions":[{"id":"https://openalex.org/I90023481","display_name":"National Institute of Information and Communications Technology","ror":"https://ror.org/016bgq349","country_code":"JP","type":"facility","lineage":["https://openalex.org/I90023481"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koji Zettsu","raw_affiliation_strings":["National Institute of Information and Communications Technology,Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Information and Communications Technology,Tokyo,Japan","institution_ids":["https://openalex.org/I90023481"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023083273"],"corresponding_institution_ids":["https://openalex.org/I90023481"],"apc_list":null,"apc_paid":null,"fwci":0.2545,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.5938847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"910","last_page":"915"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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":1.0,"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/T10524","display_name":"Traffic control and management","score":0.9975000023841858,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9968000054359436,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.734021782875061},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.6767922043800354},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.49622732400894165},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4357985854148865},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36906301975250244},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.2578660249710083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17512038350105286},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1310548186302185}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.734021782875061},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.6767922043800354},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.49622732400894165},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4357985854148865},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36906301975250244},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.2578660249710083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17512038350105286},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1310548186302185},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2624190409","https://openalex.org/W2806382623","https://openalex.org/W2911511606","https://openalex.org/W2933852525","https://openalex.org/W2981219037","https://openalex.org/W2997240588","https://openalex.org/W3007888708","https://openalex.org/W3010737657","https://openalex.org/W3015712039","https://openalex.org/W3021810927","https://openalex.org/W3034944009","https://openalex.org/W3085396922","https://openalex.org/W3110139576","https://openalex.org/W3131205408","https://openalex.org/W6774763206","https://openalex.org/W6786786545"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"Nowadays,":[0],"resolving":[1],"chaotic":[2],"traffic":[3,9,22,69,76,148,161,199],"situations,":[4],"which":[5,89,186],"usually":[6,92],"link":[7],"to":[8,19,31,43,56,74,113,118,131,144,173,179],"congestion,":[10],"is":[11,39,164,207],"an":[12],"essential":[13],"need.":[14],"It":[15],"poses":[16],"many":[17],"risks":[18],"commuters":[20],"like":[21],"accidents,":[23],"especially":[24],"during":[25],"bad":[26],"weather":[27],"situations.":[28],"Besides,":[29],"owing":[30],"the":[32,60,139,174,181,192,204],"exponential":[33],"growth":[34],"of":[35,48,183],"IoT":[36],"technologies,":[37],"it":[38],"easier":[40],"than":[41],"ever":[42],"collect":[44],"a":[45,54,128,151,155,168],"huge":[46],"amount":[47],"urban":[49],"sensing":[50],"data.":[51,194],"Therefore,":[52],"building":[53],"system":[55],"anticipate":[57],"congestion":[58,91,149,162,188,200],"from":[59],"collected":[61],"data":[62,163,201],"could":[63],"enhance":[64],"public":[65],"safety":[66],"and":[67,116,121,171],"give":[68],"police":[70],"forces":[71],"enough":[72],"time":[73,157],"handle":[75],"flows":[77],"in":[78,88,185,191],"potentially":[79],"dangerous":[80],"areas.":[81],"Moreover,":[82],"if":[83],"we":[84,94],"can":[85,95,109],"discover":[86],"patterns":[87],"predicted":[90,160,193],"happens,":[93],"build":[96],"reaction":[97],"plans":[98,123],"with":[99],"various":[100],"alert":[101],"codes.":[102],"They":[103],"create":[104],"dynamic":[105],"risk":[106],"maps":[107],"that":[108,203],"provide":[110],"useful":[111],"knowledge":[112],"both":[114],"authorities":[115],"travelers":[117],"make":[119],"rescue":[120],"travel":[122],"effectively.":[124],"This":[125],"paper":[126],"proposes":[127],"novel":[129],"framework":[130,137,206],"address":[132],"these":[133],"problems.":[134],"The":[135,159],"proposed":[136,205],"employs":[138],"Enhanced-Fusion-3DCNN":[140],"deep":[141],"learning":[142],"model":[143],"predict":[145],"future":[146],"long-term":[147],"on":[150,197],"particular":[152,156],"mesh-code":[153,184],"at":[154],"instance.":[158],"later":[165],"transformed":[166],"into":[167],"temporal":[169],"database":[170],"feed":[172],"maximal":[175],"periodic-frequent":[176],"pattern":[177],"algorithm":[178],"identify":[180],"sets":[182],"regular":[187],"may":[189],"happen":[190],"Experimental":[195],"results":[196],"real-world":[198],"demonstrate":[202],"efficient.":[208]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}