{"id":"https://openalex.org/W3007532555","doi":"https://doi.org/10.1109/bigdata47090.2019.9005985","title":"Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data","display_name":"Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007532555","doi":"https://doi.org/10.1109/bigdata47090.2019.9005985","mag":"3007532555"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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/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":true,"raw_author_name":"Ngoc-Thanh Nguyen","raw_affiliation_strings":["University of Information Technology, Vietnam National University at HCM city, HCM city, Vietnam"],"affiliations":[{"raw_affiliation_string":"University of Information Technology, Vietnam National University at HCM city, HCM city, Vietnam","institution_ids":["https://openalex.org/I123565023"]}]},{"author_position":"middle","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":false,"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":"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":3,"corresponding_author_ids":["https://openalex.org/A5047517734"],"corresponding_institution_ids":["https://openalex.org/I123565023"],"apc_list":null,"apc_paid":null,"fwci":0.3239,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.65717831,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1669","last_page":"1674"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9921000003814697,"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"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9916999936103821,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.8587799072265625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7612195014953613},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5959555506706238},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5655206441879272},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.5225031971931458},{"id":"https://openalex.org/keywords/raster-graphics","display_name":"Raster graphics","score":0.4801037609577179},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4580361843109131},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43542319536209106},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.434505820274353},{"id":"https://openalex.org/keywords/predictive-analytics","display_name":"Predictive analytics","score":0.41444632411003113},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3283594846725464}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8587799072265625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7612195014953613},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5959555506706238},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5655206441879272},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.5225031971931458},{"id":"https://openalex.org/C181844469","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster graphics","level":2,"score":0.4801037609577179},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4580361843109131},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43542319536209106},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.434505820274353},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.41444632411003113},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3283594846725464},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005985","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 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","score":0.8199999928474426,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2040297119","https://openalex.org/W2131793314","https://openalex.org/W2274750026","https://openalex.org/W2531473348","https://openalex.org/W2604610444","https://openalex.org/W2626818931","https://openalex.org/W2782846323","https://openalex.org/W2785818537","https://openalex.org/W2799109291","https://openalex.org/W2808871417","https://openalex.org/W2891280833","https://openalex.org/W2894924324","https://openalex.org/W2911511606","https://openalex.org/W2947790787","https://openalex.org/W2955735737","https://openalex.org/W2964068654","https://openalex.org/W2964273806","https://openalex.org/W2968259729","https://openalex.org/W4293455017","https://openalex.org/W6728411086","https://openalex.org/W6751923770","https://openalex.org/W6766714510"],"related_works":["https://openalex.org/W2570647323","https://openalex.org/W2206805568","https://openalex.org/W2076942471","https://openalex.org/W2863268765","https://openalex.org/W3027285423","https://openalex.org/W2896245927","https://openalex.org/W4205879366","https://openalex.org/W1961101704","https://openalex.org/W4254129905","https://openalex.org/W2414447594"],"abstract_inverted_index":{"Predictive":[0],"analytics":[1],"are":[2],"concerned":[3],"as":[4,173,185],"a":[5,12,23,31,43,143],"type":[6],"of":[7,25,45,61,69,89,106,135,181],"complex":[8,13,33,144],"event":[9,14,34,145],"processing":[10],"where":[11],"can":[15,120,146],"be":[16,121,147],"predicted":[17,122],"by":[18,112],"utilizing":[19],"insights":[20],"extracted":[21],"from":[22,49,139],"set":[24,44],"related":[26,46,136],"events.":[27],"This":[28],"paper":[29,63],"introduces":[30],"new":[32],"analysis":[35],"for":[36],"traffic":[37,56,118],"risk":[38],"prediction":[39],"using":[40],"3D-CNN":[41,90],"and":[42,76,127,131,165,176],"events":[47,137],"detected":[48,138],"multi-sources":[50,70],"urban":[51,71],"sensing":[52,72],"data":[53,73,98],"(e.g.,":[54],"congestion,":[55],"accident,":[57],"precipitation).":[58],"The":[59,133,150,167],"contribution":[60],"this":[62],"involves":[64],"(1)":[65],"the":[66,96,104,156,179,182],"spatio-temporal":[67],"information":[68],"is":[74,110,153],"reserved":[75],"wrapped":[77],"into":[78],"3D":[79],"raster":[80],"images":[81],"towards":[82],"being":[83],"able":[84],"to":[85,91,169],"leverage":[86],"recent":[87],"developments":[88],"conduct":[92],"predictive":[93],"analytics,":[94],"(2)":[95],"imbalanced":[97],"problem":[99],"which":[100],"could":[101],"severely":[102],"affect":[103],"performance":[105],"deep":[107],"learning":[108],"models":[109],"tackled":[111],"straightening":[113],"curved":[114],"geographic":[115],"chains,":[116],"(3)":[117],"risks":[119],"well":[123],"in":[124,160],"both":[125],"short-term":[126],"medium-term":[128],"time":[129],"horizons,":[130],"(4)":[132],"influence":[134],"extra":[140],"factors":[141],"on":[142,155],"explained":[148],"explicitly.":[149],"proposed":[151,183],"method":[152,184],"evaluated":[154],"real":[157],"dataset":[158],"collected":[159],"Kobe,":[161],"Japan":[162],"during":[163],"2014":[164],"2015.":[166],"comparisons":[168],"baseline":[170],"methods":[171],"such":[172],"historical":[174],"average":[175],"2D-CNN":[177],"show":[178],"advantage":[180],"well.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}