{"id":"https://openalex.org/W3034944009","doi":"https://doi.org/10.1109/tkde.2020.3001195","title":"A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges","display_name":"A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3034944009","doi":"https://doi.org/10.1109/tkde.2020.3001195","mag":"3034944009"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2020.3001195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3001195","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/journal_contribution/A_Survey_on_Modern_Deep_Neural_Network_for_Traffic_Prediction_Trends_Methods_and_Challenges/27533406","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050220402","display_name":"David Alexander Tedjopurnomo","orcid":"https://orcid.org/0000-0002-8187-2737"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"David Alexander Tedjopurnomo","raw_affiliation_strings":["RMIT University, Melbourne, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Melbourne, Victoria, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080660416","display_name":"Zhifeng Bao","orcid":"https://orcid.org/0000-0003-2477-381X"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Zhifeng Bao","raw_affiliation_strings":["RMIT University, Melbourne, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Melbourne, Victoria, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050328715","display_name":"Baihua Zheng","orcid":"https://orcid.org/0000-0001-9792-9171"},"institutions":[{"id":"https://openalex.org/I79891267","display_name":"Singapore Management University","ror":"https://ror.org/050qmg959","country_code":"SG","type":"education","lineage":["https://openalex.org/I79891267"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Baihua Zheng","raw_affiliation_strings":["Singapore Management Univerity, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Singapore Management Univerity, Singapore","institution_ids":["https://openalex.org/I79891267"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015352125","display_name":"Farhana Choudhury","orcid":"https://orcid.org/0000-0001-6529-4220"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Farhana Choudhury","raw_affiliation_strings":["University of Melbourne, Parkville, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Melbourne, Parkville, Victoria, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006614329","display_name":"A. K. Qin","orcid":"https://orcid.org/0000-0001-6631-1651"},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"A. K. Qin","raw_affiliation_strings":["Swinburne University of Technology, Hawthorn, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Swinburne University of Technology, Hawthorn, Victoria, Australia","institution_ids":["https://openalex.org/I57093077"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":19.0915,"has_fulltext":false,"cited_by_count":394,"citation_normalized_percentile":{"value":0.99815582,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"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":0.9973000288009644,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9959999918937683,"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/deep-learning","display_name":"Deep learning","score":0.7773033380508423},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7552332878112793},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.6608026027679443},{"id":"https://openalex.org/keywords/popularity","display_name":"Popularity","score":0.6543688178062439},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.6313949823379517},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6132673025131226},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5907436609268188},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.5608881115913391},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5173101425170898},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47617214918136597},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.14051514863967896}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7773033380508423},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7552332878112793},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.6608026027679443},{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.6543688178062439},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.6313949823379517},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6132673025131226},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5907436609268188},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.5608881115913391},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5173101425170898},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47617214918136597},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.14051514863967896},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/tkde.2020.3001195","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3001195","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"},{"id":"pmh:oai:ink.library.smu.edu.sg:sis_research-6998","is_oa":false,"landing_page_url":"https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6998&context=sis_research","pdf_url":null,"source":{"id":"https://openalex.org/S4306401925","display_name":"Singapore Management University Institutional Knowledge (InK) (Singapore Management University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I79891267","host_organization_name":"Singapore Management University","host_organization_lineage":["https://openalex.org/I79891267"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1109/TKDE.2020.3001195","raw_type":"Journal Article"},{"id":"pmh:oai:alma.61RMIT_INST:11254721420001341","is_oa":false,"landing_page_url":"http://purl.org/au-research/grants/arc/DP180102050","pdf_url":null,"source":{"id":"https://openalex.org/S4306402074","display_name":"RMIT Research Repository (RMIT University Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I82951845","host_organization_name":"RMIT University","host_organization_lineage":["https://openalex.org/I82951845"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:figshare.com:article/27533406","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/A_Survey_on_Modern_Deep_Neural_Network_for_Traffic_Prediction_Trends_Methods_and_Challenges/27533406","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"pmh:oai:researchbank.swinburne.edu.au:89f92ce8-a91b-47bf-9567-7016b468142e/1","is_oa":false,"landing_page_url":"http://hdl.handle.net/1959.3/456381","pdf_url":null,"source":{"id":"https://openalex.org/S4306401157","display_name":"Swinburne Research Bank (Swinburne University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I57093077","host_organization_name":"Swinburne University of Technology","host_organization_lineage":["https://openalex.org/I57093077"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Knowledge and Data Engineering (2020), pp. 1-1","raw_type":null}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/27533406","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/A_Survey_on_Modern_Deep_Neural_Network_for_Traffic_Prediction_Trends_Methods_and_Challenges/27533406","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},"sustainable_development_goals":[{"score":0.8199999928474426,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2159723965","display_name":"Data-driven Traffic Analytics for Incident Analysis and Management","funder_award_id":"LP180100114","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G2611822147","display_name":"Continuous intent tracking for virtual assistance using big contextual data","funder_award_id":"DP180102050","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"},{"id":"https://openalex.org/G3698760113","display_name":"Next-generation Intelligent Explorations of Geo-located Data ","funder_award_id":"DP200102611","funder_id":"https://openalex.org/F4320334704","funder_display_name":"Australian Research Council"}],"funders":[{"id":"https://openalex.org/F4320309327","display_name":"Google","ror":"https://ror.org/00njsd438"},{"id":"https://openalex.org/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":93,"referenced_works":["https://openalex.org/W134527144","https://openalex.org/W626441390","https://openalex.org/W641564171","https://openalex.org/W744439916","https://openalex.org/W1529430712","https://openalex.org/W1895577753","https://openalex.org/W1967444754","https://openalex.org/W1973943669","https://openalex.org/W2000100715","https://openalex.org/W2004353783","https://openalex.org/W2027392238","https://openalex.org/W2030444281","https://openalex.org/W2036785686","https://openalex.org/W2040297119","https://openalex.org/W2064675550","https://openalex.org/W2073640212","https://openalex.org/W2082533141","https://openalex.org/W2083238230","https://openalex.org/W2097498150","https://openalex.org/W2099471712","https://openalex.org/W2110798204","https://openalex.org/W2111991989","https://openalex.org/W2131767615","https://openalex.org/W2132711183","https://openalex.org/W2136848157","https://openalex.org/W2150152686","https://openalex.org/W2160507653","https://openalex.org/W2163089819","https://openalex.org/W2165991108","https://openalex.org/W2190353863","https://openalex.org/W2439965388","https://openalex.org/W2528639018","https://openalex.org/W2533328922","https://openalex.org/W2550245413","https://openalex.org/W2560675361","https://openalex.org/W2563118655","https://openalex.org/W2573587735","https://openalex.org/W2579495707","https://openalex.org/W2583466634","https://openalex.org/W2603433898","https://openalex.org/W2604472983","https://openalex.org/W2613331518","https://openalex.org/W2616134560","https://openalex.org/W2624190409","https://openalex.org/W2724431948","https://openalex.org/W2735405280","https://openalex.org/W2756203131","https://openalex.org/W2782977972","https://openalex.org/W2787772181","https://openalex.org/W2791999218","https://openalex.org/W2792440155","https://openalex.org/W2793820729","https://openalex.org/W2794283254","https://openalex.org/W2802508687","https://openalex.org/W2806382623","https://openalex.org/W2809366716","https://openalex.org/W2884128153","https://openalex.org/W2884604806","https://openalex.org/W2885178111","https://openalex.org/W2906175158","https://openalex.org/W2908455080","https://openalex.org/W2912407321","https://openalex.org/W2912984848","https://openalex.org/W2944500953","https://openalex.org/W2950817888","https://openalex.org/W2962834725","https://openalex.org/W2963358464","https://openalex.org/W2963403868","https://openalex.org/W2963440544","https://openalex.org/W2963995014","https://openalex.org/W2974087501","https://openalex.org/W2978023058","https://openalex.org/W2981104679","https://openalex.org/W2996451395","https://openalex.org/W3000386982","https://openalex.org/W3041279471","https://openalex.org/W3102632991","https://openalex.org/W3103720336","https://openalex.org/W3164700307","https://openalex.org/W3209764242","https://openalex.org/W4245422202","https://openalex.org/W6605505536","https://openalex.org/W6619978402","https://openalex.org/W6620883322","https://openalex.org/W6622008427","https://openalex.org/W6730235577","https://openalex.org/W6739901393","https://openalex.org/W6741439554","https://openalex.org/W6746015598","https://openalex.org/W6748280707","https://openalex.org/W6748850308","https://openalex.org/W6770493130","https://openalex.org/W6773017188"],"related_works":["https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W4294565801","https://openalex.org/W2170801710","https://openalex.org/W2952704802","https://openalex.org/W2741781807"],"abstract_inverted_index":{"In":[0,137],"this":[1,138,201],"modern":[2],"era,":[3],"traffic":[4,30,35,41,128,150,168],"congestion":[5,31],"has":[6,43],"become":[7],"a":[8,155,192],"major":[9],"source":[10],"of":[11,23,40,78,93,119,127,132,145,158,181],"severe":[12],"negative":[13],"economic":[14],"and":[15,64,81,85,113,173,184,189,197],"environmental":[16],"impact":[17],"for":[18,149,200],"urban":[19],"areas":[20],"worldwide.":[21],"One":[22],"the":[24,51,91,111,117,125,167,175,182,195],"most":[25],"efficient":[26],"ways":[27],"to":[28,71,101,110],"mitigate":[29],"is":[32],"through":[33],"future":[34,198],"prediction.":[36,151],"The":[37],"research":[38],"field":[39,126],"prediction":[42,104,170],"evolved":[44],"greatly":[45],"ever":[46],"since":[47],"its":[48,65,102],"inception":[49],"in":[50,124,166],"late":[52],"70s.":[53],"Earlier":[54],"studies":[55],"mainly":[56],"use":[57],"classical":[58],"statistical":[59],"models":[60,76,123],"such":[61,133],"as":[62],"ARIMA":[63],"variants.":[66],"Recently,":[67],"researchers":[68],"have":[69],"started":[70],"focus":[72],"on":[73],"machine":[74],"learning":[75],"because":[77],"their":[79],"power":[80,105],"flexibility.":[82],"As":[83],"theoretical":[84],"technological":[86],"advances":[87],"emerge,":[88],"we":[89,140],"enter":[90],"era":[92],"deep":[94,114,120,146,160],"neural":[95,121,147,161],"network,":[96],"which":[97,106],"gained":[98],"popularity":[99,118],"due":[100],"sheer":[103],"can":[107],"be":[108],"attributed":[109],"complex":[112],"structure.":[115],"Despite":[116],"network":[122,148,162],"prediction,":[129],"literature":[130],"surveys":[131],"methods":[134],"are":[135],"rare.":[136],"work,":[139],"present":[141,178],"an":[142,179],"up-to-date":[143],"survey":[144],"We":[152],"will":[153],"provide":[154,191],"detailed":[156],"explanation":[157],"popular":[159],"architectures":[163],"commonly":[164],"used":[165],"flow":[169],"literatures,":[171],"categorize":[172],"describe":[174],"literatures":[176],"themselves,":[177],"overview":[180],"commonalities":[183],"differences":[185],"among":[186],"different":[187],"works,":[188],"finally":[190],"discussion":[193],"regarding":[194],"challenges":[196],"directions":[199],"field.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":11},{"year":2025,"cited_by_count":94},{"year":2024,"cited_by_count":118},{"year":2023,"cited_by_count":88},{"year":2022,"cited_by_count":56},{"year":2021,"cited_by_count":25},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-20T07:56:41.581041","created_date":"2025-10-10T00:00:00"}