{"id":"https://openalex.org/W4285260582","doi":"https://doi.org/10.1109/tits.2022.3179391","title":"Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning","display_name":"Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning","publication_year":2022,"publication_date":"2022-06-10","ids":{"openalex":"https://openalex.org/W4285260582","doi":"https://doi.org/10.1109/tits.2022.3179391"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2022.3179391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3179391","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","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/A5004583236","display_name":"Mengran Xia","orcid":"https://orcid.org/0000-0003-1592-847X"},"institutions":[{"id":"https://openalex.org/I158934434","display_name":"Zhongnan University of Economics and Law","ror":"https://ror.org/04yqxxq63","country_code":"CN","type":"education","lineage":["https://openalex.org/I158934434"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mengran Xia","raw_affiliation_strings":["School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-1592-847X","affiliations":[{"raw_affiliation_string":"School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China","institution_ids":["https://openalex.org/I158934434"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016193325","display_name":"Dawei Jin","orcid":"https://orcid.org/0000-0002-5922-2746"},"institutions":[{"id":"https://openalex.org/I158934434","display_name":"Zhongnan University of Economics and Law","ror":"https://ror.org/04yqxxq63","country_code":"CN","type":"education","lineage":["https://openalex.org/I158934434"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Jin","raw_affiliation_strings":["School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-5922-2746","affiliations":[{"raw_affiliation_string":"School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China","institution_ids":["https://openalex.org/I158934434"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100338185","display_name":"Jingyu Chen","orcid":"https://orcid.org/0000-0001-9941-8033"},"institutions":[{"id":"https://openalex.org/I158934434","display_name":"Zhongnan University of Economics and Law","ror":"https://ror.org/04yqxxq63","country_code":"CN","type":"education","lineage":["https://openalex.org/I158934434"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Chen","raw_affiliation_strings":["School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-9941-8033","affiliations":[{"raw_affiliation_string":"School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China","institution_ids":["https://openalex.org/I158934434"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5004583236"],"corresponding_institution_ids":["https://openalex.org/I158934434"],"apc_list":null,"apc_paid":null,"fwci":11.451,"has_fulltext":false,"cited_by_count":124,"citation_normalized_percentile":{"value":0.99404084,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"24","issue":"1","first_page":"1191","last_page":"1203"},"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":0.996999979019165,"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":0.9818000197410583,"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":0.7730139493942261},{"id":"https://openalex.org/keywords/subnetwork","display_name":"Subnetwork","score":0.684036910533905},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.6073038578033447},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.583781361579895},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5725262761116028},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5119746923446655},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.4960049092769623},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.489541620016098},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4775834381580353},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.46310514211654663},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4588634669780731},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44760861992836},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42628616094589233},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.16514095664024353},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.13714277744293213},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11307588219642639}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7730139493942261},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.684036910533905},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.6073038578033447},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.583781361579895},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5725262761116028},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5119746923446655},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.4960049092769623},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.489541620016098},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4775834381580353},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.46310514211654663},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4588634669780731},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44760861992836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42628616094589233},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.16514095664024353},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.13714277744293213},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11307588219642639},{"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/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2022.3179391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2022.3179391","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2846296007","display_name":null,"funder_award_id":"YRTD202214","funder_id":"https://openalex.org/F4320310114","funder_display_name":"Zhongnan University of Economics and Law"},{"id":"https://openalex.org/G4569406673","display_name":null,"funder_award_id":"2722021AJ001","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8363558271","display_name":null,"funder_award_id":"XKRH202101","funder_id":"https://openalex.org/F4320310114","funder_display_name":"Zhongnan University of Economics and Law"}],"funders":[{"id":"https://openalex.org/F4320310114","display_name":"Zhongnan University of Economics and Law","ror":"https://ror.org/04yqxxq63"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W158283968","https://openalex.org/W239469043","https://openalex.org/W1967444754","https://openalex.org/W2009797633","https://openalex.org/W2036785686","https://openalex.org/W2049952439","https://openalex.org/W2057918527","https://openalex.org/W2060570510","https://openalex.org/W2069929199","https://openalex.org/W2075407851","https://openalex.org/W2090192376","https://openalex.org/W2111991989","https://openalex.org/W2115032462","https://openalex.org/W2131681506","https://openalex.org/W2131819535","https://openalex.org/W2136922672","https://openalex.org/W2139606794","https://openalex.org/W2145039203","https://openalex.org/W2150010190","https://openalex.org/W2150152686","https://openalex.org/W2160507653","https://openalex.org/W2165991108","https://openalex.org/W2297152540","https://openalex.org/W2398137098","https://openalex.org/W2530417694","https://openalex.org/W2535838896","https://openalex.org/W2593182953","https://openalex.org/W2613331518","https://openalex.org/W2740759433","https://openalex.org/W2793423901","https://openalex.org/W2793820729","https://openalex.org/W2889230014","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2912213068","https://openalex.org/W2919115771","https://openalex.org/W2935726879","https://openalex.org/W2955819484","https://openalex.org/W2964015378","https://openalex.org/W3001437801","https://openalex.org/W3008849576","https://openalex.org/W3010852232","https://openalex.org/W3010941533","https://openalex.org/W3025058555","https://openalex.org/W3026534984","https://openalex.org/W3080284469","https://openalex.org/W3099768174","https://openalex.org/W3101220048","https://openalex.org/W3112885687","https://openalex.org/W3114955313","https://openalex.org/W3126441351","https://openalex.org/W3154667344","https://openalex.org/W4205538115","https://openalex.org/W4206991310","https://openalex.org/W4297687186","https://openalex.org/W4297733535","https://openalex.org/W4318619660","https://openalex.org/W6606592145","https://openalex.org/W6609240507","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6728757088","https://openalex.org/W6794274047"],"related_works":["https://openalex.org/W2060724872","https://openalex.org/W2082094785","https://openalex.org/W2202198356","https://openalex.org/W3087203342","https://openalex.org/W2377184161","https://openalex.org/W228984114","https://openalex.org/W4226360758","https://openalex.org/W2151093953","https://openalex.org/W2090026684","https://openalex.org/W2907567977"],"abstract_inverted_index":{"This":[0],"study":[1],"proposes":[2],"a":[3,16,102,163],"short-term":[4,130,172],"traffic":[5,53,61,173],"flow":[6,174],"prediction":[7,76,105,146],"model":[8,98,104,161],"that":[9,120],"combines":[10],"community":[11,44,86],"detection-based":[12],"federated":[13,43],"learning":[14],"with":[15,85,153,176],"graph":[17,139],"convolutional":[18,133],"network":[19,135,141],"(GCN)":[20],"to":[21,149],"alleviate":[22],"the":[23,37,57,67,82,92,96,108,114,121,128,150,155,159],"time-consuming":[24],"training,":[25],"higher":[26],"communication":[27],"costs,":[28],"and":[29,51,100,116,138,170,181],"data":[30,40],"privacy":[31],"risks":[32],"of":[33,39,59,70],"global":[34,93,103,160],"GCNs":[35],"as":[36,158],"amount":[38],"increases.":[41],"The":[42,74,144],"GCN":[45],"(FCGCN)":[46],"can":[47],"achieve":[48],"timely,":[49],"accurate,":[50],"safe":[52],"state":[54],"predictions":[55,175],"in":[56],"era":[58],"big":[60],"data,":[62],"which":[63],"is":[64,147],"critical":[65],"for":[66],"efficient":[68],"operation":[69],"intelligent":[71],"transportation":[72],"systems.":[73],"FCGCN":[75,122,145],"process":[77],"has":[78],"four":[79,124],"steps:":[80],"dividing":[81],"local":[83,88,97],"subnetwork":[84],"detection,":[87],"training":[89],"based":[90,106],"on":[91,107,113],"parameters,":[94,99],"uploading":[95],"constructing":[101],"aggregated":[109],"parameters.":[110],"Numerical":[111],"results":[112],"PeMS04":[115],"PeMS08":[117],"datasets":[118],"show":[119],"outperforms":[123],"benchmark":[125],"models,":[126],"namely,":[127],"long":[129],"memory":[131],"(LSTM),":[132],"neural":[134],"(CNN),":[136],"ChebNet,":[137],"attention":[140],"(GAT)":[142],"models.":[143],"closer":[148],"real":[151],"value,":[152],"nearly":[154],"same":[156],"performance":[157],"at":[162],"lower":[164],"time":[165],"cost,":[166],"thus":[167],"achieving":[168],"accurate":[169],"secure":[171],"three":[177],"parameters:":[178],"flow,":[179],"speed,":[180],"occupancy.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":18},{"year":2025,"cited_by_count":49},{"year":2024,"cited_by_count":37},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-05T09:01:59.212387","created_date":"2025-10-10T00:00:00"}
