{"id":"https://openalex.org/W2886287742","doi":"https://doi.org/10.1109/mnet.2018.1700411","title":"Urban Traffic Prediction from Mobility Data Using Deep Learning","display_name":"Urban Traffic Prediction from Mobility Data Using Deep Learning","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2886287742","doi":"https://doi.org/10.1109/mnet.2018.1700411","mag":"2886287742"},"language":"en","primary_location":{"id":"doi:10.1109/mnet.2018.1700411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mnet.2018.1700411","pdf_url":null,"source":{"id":"https://openalex.org/S186584794","display_name":"IEEE Network","issn_l":"0890-8044","issn":["0890-8044","1558-156X"],"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 Network","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/A5077210145","display_name":"Zhidan Liu","orcid":"https://orcid.org/0000-0002-4411-7644"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhidan Liu","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100419083","display_name":"Zhenjiang Li","orcid":"https://orcid.org/0000-0002-3296-3392"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zhenjiang Li","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001188748","display_name":"Kaishun Wu","orcid":"https://orcid.org/0000-0003-2216-0737"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaishun Wu","raw_affiliation_strings":["College of Computer Science and Software Engineering, Shenzhen University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Software Engineering, Shenzhen University","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100361458","display_name":"Mo Li","orcid":"https://orcid.org/0000-0002-6047-9709"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Mo Li","raw_affiliation_strings":["School of Computer Science and Engineering, Nanyang Technological University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanyang Technological University","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5077210145"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":15.3368,"has_fulltext":false,"cited_by_count":163,"citation_normalized_percentile":{"value":0.99459318,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"32","issue":"4","first_page":"40","last_page":"46"},"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":0.9905999898910522,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9894999861717224,"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":0.8088058233261108},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6966782808303833},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6156896352767944},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4927866756916046},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4868170917034149},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4818957448005676},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4647039473056793},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.45992594957351685},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4527268409729004},{"id":"https://openalex.org/keywords/floating-car-data","display_name":"Floating car data","score":0.4504292607307434},{"id":"https://openalex.org/keywords/urban-computing","display_name":"Urban computing","score":0.4464855194091797},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.39788174629211426},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33486509323120117},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.31387943029403687},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.25296550989151},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.15717414021492004},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.13044100999832153},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09625840187072754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8088058233261108},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6966782808303833},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6156896352767944},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4927866756916046},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4868170917034149},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4818957448005676},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4647039473056793},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.45992594957351685},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4527268409729004},{"id":"https://openalex.org/C64093975","wikidata":"https://www.wikidata.org/wiki/Q356677","display_name":"Floating car data","level":3,"score":0.4504292607307434},{"id":"https://openalex.org/C2778459138","wikidata":"https://www.wikidata.org/wiki/Q7900107","display_name":"Urban computing","level":2,"score":0.4464855194091797},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.39788174629211426},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33486509323120117},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.31387943029403687},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.25296550989151},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.15717414021492004},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.13044100999832153},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09625840187072754},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/mnet.2018.1700411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mnet.2018.1700411","pdf_url":null,"source":{"id":"https://openalex.org/S186584794","display_name":"IEEE Network","issn_l":"0890-8044","issn":["0890-8044","1558-156X"],"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 Network","raw_type":"journal-article"},{"id":"pmh:oai:dr.ntu.edu.sg:10356/140307","is_oa":false,"landing_page_url":"https://hdl.handle.net/10356/140307","pdf_url":null,"source":{"id":"https://openalex.org/S4306402609","display_name":"DR-NTU (Nanyang Technological University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I172675005","host_organization_name":"Nanyang Technological University","host_organization_lineage":["https://openalex.org/I172675005"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal Article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-131940","is_oa":false,"landing_page_url":"http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=LinksAMR&SrcApp=PARTNER_APP&DestLinkType=FullRecord&DestApp=WOS&KeyUT=000440855200008","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.800000011920929,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1968311321","https://openalex.org/W2031896749","https://openalex.org/W2036785686","https://openalex.org/W2040297119","https://openalex.org/W2060393849","https://openalex.org/W2062017159","https://openalex.org/W2343480289","https://openalex.org/W2528639018","https://openalex.org/W2531473348","https://openalex.org/W2563118655","https://openalex.org/W2572984335","https://openalex.org/W2579495707","https://openalex.org/W2583466634","https://openalex.org/W2624190409","https://openalex.org/W2919115771","https://openalex.org/W6659849045","https://openalex.org/W6728411086","https://openalex.org/W6728547873","https://openalex.org/W6730654704","https://openalex.org/W6731786745"],"related_works":["https://openalex.org/W2009112536","https://openalex.org/W2410941711","https://openalex.org/W2047739511","https://openalex.org/W432084041","https://openalex.org/W2963251637","https://openalex.org/W2986732134","https://openalex.org/W2587362999","https://openalex.org/W1977405947","https://openalex.org/W1977153226","https://openalex.org/W2008840139"],"abstract_inverted_index":{"Traffic":[0],"information":[1],"is":[2],"of":[3,12,43,58,85,90,134],"great":[4],"importance":[5],"for":[6,18,71,94],"urban":[7,13,135],"cities,":[8],"and":[9,39,61,82,100,107,132],"accurate":[10],"prediction":[11,23,137],"traffics":[14],"has":[15],"been":[16],"pursued":[17],"many":[19],"years.":[20],"Urban":[21],"traffic":[22,32,48,73,92,96,98,136],"aims":[24],"to":[25,29,46,54,128],"exploit":[26],"sophisticated":[27],"models":[28,45],"capture":[30],"hidden":[31],"characteristics":[33],"from":[34],"substantial":[35],"historical":[36],"mobility":[37],"data":[38],"then":[40],"makes":[41],"use":[42],"trained":[44],"predict":[47],"conditions":[49],"in":[50,88],"the":[51,55,80,130],"future.":[52],"Due":[53],"powerful":[56],"capabilities":[57],"representation":[59],"learning":[60,66,87],"feature":[62],"extraction,":[63],"emerging":[64],"deep":[65,86],"becomes":[67],"a":[68,139],"potent":[69],"alternative":[70],"such":[72],"modeling.":[74],"In":[75,103],"this":[76],"article,":[77],"we":[78,105,122],"envision":[79],"potential":[81],"broard":[83],"usage":[84],"predictions":[89],"various":[91],"indicators,":[93],"example,":[95],"speed,":[97],"flow,":[99],"accident":[101],"risk.":[102],"addition,":[104],"summarize":[106],"analyze":[108],"some":[109],"early":[110],"attempts":[111],"that":[112],"have":[113],"achieved":[114],"notable":[115],"performance.":[116],"By":[117],"discussing":[118],"these":[119],"existing":[120],"advances,":[121],"propose":[123],"two":[124],"future":[125],"research":[126],"directions":[127],"improve":[129],"accuracy":[131],"efficiency":[133],"on":[138],"large":[140],"scale.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":20},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":30},{"year":2020,"cited_by_count":31},{"year":2019,"cited_by_count":17},{"year":2018,"cited_by_count":1}],"updated_date":"2026-05-19T08:33:51.333923","created_date":"2025-10-10T00:00:00"}