{"id":"https://openalex.org/W4294690802","doi":"https://doi.org/10.1109/access.2022.3204036","title":"GCN-GAN: Integrating Graph Convolutional Network and Generative Adversarial Network for Traffic Flow Prediction","display_name":"GCN-GAN: Integrating Graph Convolutional Network and Generative Adversarial Network for Traffic Flow Prediction","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294690802","doi":"https://doi.org/10.1109/access.2022.3204036"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3204036","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3204036","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09875268.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09875268.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077318647","display_name":"Hongling Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I76214153","display_name":"Lanzhou University","ror":"https://ror.org/01mkqqe32","country_code":"CN","type":"education","lineage":["https://openalex.org/I76214153"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hongling Zheng","raw_affiliation_strings":["School of Information Science and Engineering, Lanzhou University, Lanzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9077-1594","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Lanzhou University, Lanzhou, China","institution_ids":["https://openalex.org/I76214153"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100331094","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0002-9851-6376"},"institutions":[{"id":"https://openalex.org/I2800372957","display_name":"China Electronics Technology Group Corporation","ror":"https://ror.org/0098hst83","country_code":"CN","type":"company","lineage":["https://openalex.org/I2800372957"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["The Fiftieth Research Institute of China Electronic Technology Group Corporation, Shanghai, Putuo, China","The fiftieth Research Institute of China Electronic Technology Group Corporation Address: No. 318, Changhe Road, Putuo District, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Fiftieth Research Institute of China Electronic Technology Group Corporation, Shanghai, Putuo, China","institution_ids":["https://openalex.org/I2800372957"]},{"raw_affiliation_string":"The fiftieth Research Institute of China Electronic Technology Group Corporation Address: No. 318, Changhe Road, Putuo District, Shanghai, China","institution_ids":["https://openalex.org/I2800372957"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100371429","display_name":"Yongfeng Li","orcid":"https://orcid.org/0000-0003-1805-3007"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongfeng Li","raw_affiliation_strings":["School of Cyberspace Security, Xi’an Jiaotong University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cyberspace Security, Xi’an Jiaotong University, Xi’an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017164626","display_name":"Ziqin Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ziqin Yan","raw_affiliation_strings":["The Silk Road Infoport Company Ltd., Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Silk Road Infoport Company Ltd., Xi’an, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021502688","display_name":"Tinghong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tinghong Li","raw_affiliation_strings":["China Mobile Communications Corporation Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Mobile Communications Corporation Ltd., Beijing, China","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5077318647"],"corresponding_institution_ids":["https://openalex.org/I76214153"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.1827,"has_fulltext":true,"cited_by_count":51,"citation_normalized_percentile":{"value":0.96409631,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"10","issue":null,"first_page":"94051","last_page":"94062"},"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/T10370","display_name":"Traffic and Road Safety","score":0.9847999811172485,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.7605514526367188},{"id":"https://openalex.org/keywords/control-flow-graph","display_name":"Control flow graph","score":0.6733718514442444},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6200659275054932},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5516937971115112},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5051794648170471},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.49654465913772583},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4710918366909027},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44468557834625244},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.441273033618927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43412837386131287},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.42830532789230347},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4279824197292328},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.41978731751441956},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41799330711364746},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3644119203090668},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3498407006263733},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.2766697406768799},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27636122703552246},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.18619561195373535},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07972142100334167}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7605514526367188},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.6733718514442444},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6200659275054932},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5516937971115112},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5051794648170471},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.49654465913772583},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4710918366909027},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44468557834625244},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.441273033618927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43412837386131287},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.42830532789230347},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4279824197292328},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.41978731751441956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41799330711364746},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3644119203090668},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3498407006263733},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2766697406768799},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27636122703552246},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.18619561195373535},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07972142100334167},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3204036","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3204036","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09875268.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:daffdc4a652e4bee87c728dd835565c3","is_oa":true,"landing_page_url":"https://doaj.org/article/daffdc4a652e4bee87c728dd835565c3","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 94051-94062 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3204036","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3204036","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09875268.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320313889","display_name":"Lanzhou University","ror":"https://ror.org/01mkqqe32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4294690802.pdf","grobid_xml":"https://content.openalex.org/works/W4294690802.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1549382001","https://openalex.org/W1991401171","https://openalex.org/W2012051283","https://openalex.org/W2083238230","https://openalex.org/W2100337289","https://openalex.org/W2116341502","https://openalex.org/W2125389028","https://openalex.org/W2135604164","https://openalex.org/W2194775991","https://openalex.org/W2516530047","https://openalex.org/W2800123561","https://openalex.org/W2805294763","https://openalex.org/W2808437996","https://openalex.org/W2810269073","https://openalex.org/W2885195348","https://openalex.org/W2890504272","https://openalex.org/W2896797507","https://openalex.org/W2901504064","https://openalex.org/W2903817453","https://openalex.org/W2907228515","https://openalex.org/W2910197777","https://openalex.org/W2910982572","https://openalex.org/W2916752133","https://openalex.org/W2948159890","https://openalex.org/W2964015378","https://openalex.org/W2964066133","https://openalex.org/W2965676388","https://openalex.org/W2972303719","https://openalex.org/W2972461593","https://openalex.org/W2995288704","https://openalex.org/W2998416884","https://openalex.org/W3007075806","https://openalex.org/W3016398929","https://openalex.org/W3021118290","https://openalex.org/W3021822145","https://openalex.org/W3022643593","https://openalex.org/W3023104975","https://openalex.org/W3024761859","https://openalex.org/W3034408619","https://openalex.org/W3035338169","https://openalex.org/W3040275518","https://openalex.org/W3042710080","https://openalex.org/W3121057636","https://openalex.org/W3132782787","https://openalex.org/W3153990350","https://openalex.org/W3155739706","https://openalex.org/W6678815747","https://openalex.org/W6726873649","https://openalex.org/W6752475655","https://openalex.org/W6774779085","https://openalex.org/W6814963641"],"related_works":["https://openalex.org/W2149721642","https://openalex.org/W2587362999","https://openalex.org/W432084041","https://openalex.org/W2394010358","https://openalex.org/W2361078351","https://openalex.org/W2986732134","https://openalex.org/W4239349137","https://openalex.org/W1463884142","https://openalex.org/W2963251637","https://openalex.org/W239469043"],"abstract_inverted_index":{"As":[0],"a":[1,19,57],"necessary":[2],"component":[3],"in":[4,18,24,45,101,155],"intelligent":[5],"transportation":[6],"systems":[7],"(ITS),":[8],"traffic":[9,16,39,59,90,98,111,135],"flow-based":[10],"prediction":[11,41,48,61,113,123],"can":[12,120],"accurately":[13],"estimate":[14],"the":[15,25,29,93,102,106,134,145],"flow":[17,40,60,99,112,136],"certain":[20],"period":[21],"and":[22,34,50,66,80,151],"area":[23],"future.":[26],"However,":[27],"despite":[28],"success":[30],"of":[31,47],"traditional":[32,127],"research":[33],"current":[35],"machine":[36],"learning":[37],"methods,":[38],"models":[42],"have":[43],"limitations":[44],"terms":[46],"accuracy":[49],"efficiency.":[51],"In":[52],"this":[53],"work,":[54],"we":[55],"propose":[56],"novel":[58],"model":[62,143],"named":[63],"Graph":[64,74],"Convolution":[65,75],"Generative":[67,81],"Adversative":[68,82],"Neural":[69,76,83],"Network":[70,77,84],"(GCN-GAN),":[71],"which":[72],"leverages":[73],"(GCN)":[78],"module":[79,86,95,108],"(GAN)":[85],"to":[87],"predict":[88],"urban":[89],"flow.":[91],"Firstly,":[92],"GCN":[94],"extracts":[96],"historical":[97],"information":[100],"graph":[103],"structure.":[104],"Secondly,":[105],"GAN":[107],"generates":[109],"reliable":[110],"results":[114,124],"by":[115,129,148],"adversative":[116],"training.":[117],"Additionally,":[118],"GCN-GAN":[119,142],"parallelly":[121],"generate":[122],"rather":[125],"than":[126],"one":[128],"one.":[130],"Through":[131],"experiments":[132],"on":[133],"dataset":[137],"at":[138],"multiple":[139],"intersections,":[140],"our":[141],"outperforms":[144],"baseline":[146],"methods":[147],"over":[149],"30.54%":[150],"has":[152],"apparent":[153],"advantages":[154],"multi-step":[156],"prediction.":[157]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}