{"id":"https://openalex.org/W4320029286","doi":"https://doi.org/10.1109/globecom48099.2022.10001710","title":"Sparse Big Data for Vehicular Network Traffic Flow Estimation: A Machine Learning Approach","display_name":"Sparse Big Data for Vehicular Network Traffic Flow Estimation: A Machine Learning Approach","publication_year":2022,"publication_date":"2022-12-04","ids":{"openalex":"https://openalex.org/W4320029286","doi":"https://doi.org/10.1109/globecom48099.2022.10001710"},"language":"en","primary_location":{"id":"doi:10.1109/globecom48099.2022.10001710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom48099.2022.10001710","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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/A5035878200","display_name":"Jianzhe Xue","orcid":"https://orcid.org/0009-0007-7278-9177"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jianzhe Xue","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100425388","display_name":"Tianqi Zhang","orcid":"https://orcid.org/0000-0002-3729-1812"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianqi Zhang","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074854979","display_name":"Wen Wu","orcid":"https://orcid.org/0000-0002-0458-1282"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Wu","raw_affiliation_strings":["Cheng Laboratory,Shenzhen,China,518000"],"affiliations":[{"raw_affiliation_string":"Cheng Laboratory,Shenzhen,China,518000","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043286765","display_name":"Haibo Zhou","orcid":"https://orcid.org/0000-0002-6549-8917"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibo Zhou","raw_affiliation_strings":["School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023"],"affiliations":[{"raw_affiliation_string":"School of Electronic Science and Engineering, Nanjing University,Nanjing,China,210023","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100773343","display_name":"Xuemin Shen","orcid":"https://orcid.org/0000-0002-4140-287X"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xuemin Shen","raw_affiliation_strings":["University of Waterloo,Department of Electrical and Computer Engineering,Waterloo,Ontario,Canada","Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Department of Electrical and Computer Engineering,Waterloo,Ontario,Canada","institution_ids":["https://openalex.org/I151746483"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5035878200"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":2.6983,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.92430442,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4959","last_page":"4963"},"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":0.9961000084877014,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.8245724439620972},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5835260152816772},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.553831934928894},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5278410911560059},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.516221284866333},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.46949270367622375},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46318885684013367},{"id":"https://openalex.org/keywords/vehicular-ad-hoc-network","display_name":"Vehicular ad hoc network","score":0.4193558096885681},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4091285765171051},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38568103313446045},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12978774309158325},{"id":"https://openalex.org/keywords/wireless-ad-hoc-network","display_name":"Wireless ad hoc network","score":0.11696645617485046},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.1161869466304779},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.08666187524795532},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08090329170227051}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8245724439620972},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5835260152816772},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.553831934928894},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5278410911560059},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.516221284866333},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.46949270367622375},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46318885684013367},{"id":"https://openalex.org/C192448918","wikidata":"https://www.wikidata.org/wiki/Q682677","display_name":"Vehicular ad hoc network","level":4,"score":0.4193558096885681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4091285765171051},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38568103313446045},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12978774309158325},{"id":"https://openalex.org/C94523657","wikidata":"https://www.wikidata.org/wiki/Q4085781","display_name":"Wireless ad hoc network","level":3,"score":0.11696645617485046},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.1161869466304779},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.08666187524795532},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08090329170227051},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom48099.2022.10001710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom48099.2022.10001710","pdf_url":null,"source":{"id":"https://openalex.org/S4363607705","display_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","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":[{"id":"https://openalex.org/G653517181","display_name":null,"funder_award_id":"61871211","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2572939427","https://openalex.org/W2778363563","https://openalex.org/W2895746990","https://openalex.org/W2901504064","https://openalex.org/W2963044698","https://openalex.org/W3014197885","https://openalex.org/W3092125438","https://openalex.org/W3109602317","https://openalex.org/W4323314209","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4394895745","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2910064364","https://openalex.org/W4200136508","https://openalex.org/W2499527417"],"abstract_inverted_index":{"Traffic":[0],"flow":[1,48],"estimation":[2,78,144],"(TFE)":[3],"plays":[4],"an":[5],"important":[6],"role":[7],"in":[8,23],"intelligent":[9],"transportation":[10],"systems":[11],"(ITS).":[12],"Considering":[13],"the":[14,32,45,77,82,89,92,108,113,118,128],"prohibitive":[15],"overhead":[16],"of":[17,49,57,85,101,132],"collecting":[18],"and":[19,69,95,111,135],"processing":[20],"massive":[21],"data":[22],"vehicular":[24,34,50,58,133],"networks,":[25],"it":[26,136],"is":[27,73,137],"more":[28],"practical":[29],"to":[30,75,106,116],"use":[31],"sparse":[33,146],"big":[35,147],"data.":[36,59,87,148],"In":[37],"this":[38],"paper,":[39],"we":[40],"focus":[41],"on":[42,127],"accurately":[43],"estimating":[44],"urban":[46],"traffic":[47,86],"networks":[51],"only":[52],"using":[53],"a":[54,99],"small":[55],"portion":[56],"A":[60],"new":[61],"spatiotemporal":[62],"machine":[63],"learning":[64],"model,":[65,98],"named":[66],"Graph":[67],"Sampling":[68],"Aggregate":[70],"Transformer":[71,114],"(GSAT),":[72],"developed":[74],"improve":[76],"accuracy":[79],"by":[80],"leveraging":[81],"inner":[83],"correlation":[84,110],"Specifically,":[88],"GSAT":[90,123,140],"uses":[91],"graph":[93,102],"sample":[94],"aggregate":[96,107],"(GraphSAGE)":[97],"variety":[100],"neural":[103],"network":[104],"(GNN),":[105],"spatial":[109],"applies":[112],"model":[115],"capture":[117],"temporal":[119],"correlation.":[120],"We":[121],"evaluate":[122],"at":[124],"multiple":[125],"sparsity":[126],"real":[129],"world":[130],"dataset":[131],"network,":[134],"demonstrated":[138],"that":[139],"can":[141],"achieve":[142],"accurate":[143],"with":[145]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}