{"id":"https://openalex.org/W2978023058","doi":"https://doi.org/10.1109/ijcnn.2019.8852211","title":"GCGAN: Generative Adversarial Nets with Graph CNN for Network-Scale Traffic Prediction","display_name":"GCGAN: Generative Adversarial Nets with Graph CNN for Network-Scale Traffic Prediction","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978023058","doi":"https://doi.org/10.1109/ijcnn.2019.8852211","mag":"2978023058"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852211","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852211","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5100319905","display_name":"Yuxuan Zhang","orcid":"https://orcid.org/0000-0002-3760-1083"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuxuan Zhang","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035708362","display_name":"Senzhang Wang","orcid":"https://orcid.org/0000-0002-3615-4859"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]},{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Senzhang Wang","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, The Hong Kong Polytechnic University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, The Hong Kong Polytechnic University, Nanjing, China","institution_ids":["https://openalex.org/I9842412","https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443103","display_name":"Bing Chen","orcid":"https://orcid.org/0000-0002-2863-5441"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Chen","raw_affiliation_strings":["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100740023","display_name":"Jiannong Cao","orcid":"https://orcid.org/0000-0002-2725-2529"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jiannong Cao","raw_affiliation_strings":["Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100319905"],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":4.2061,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.93730119,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9958999752998352,"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":0.9894999861717224,"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":0.7890129089355469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5370617508888245},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5050066113471985},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5021648406982422},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47304266691207886},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.47207120060920715},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47059595584869385},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.44488978385925293},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.43191128969192505},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4245627522468567},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.41698652505874634},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.21841463446617126},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.14083051681518555}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7890129089355469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5370617508888245},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5050066113471985},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5021648406982422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47304266691207886},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.47207120060920715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47059595584869385},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.44488978385925293},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.43191128969192505},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4245627522468567},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.41698652505874634},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.21841463446617126},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.14083051681518555},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852211","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852211","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W3830626","https://openalex.org/W590086890","https://openalex.org/W613760533","https://openalex.org/W1915929007","https://openalex.org/W1968632832","https://openalex.org/W1973943669","https://openalex.org/W1982978808","https://openalex.org/W2011504567","https://openalex.org/W2040297119","https://openalex.org/W2062017159","https://openalex.org/W2079662306","https://openalex.org/W2090192376","https://openalex.org/W2097498150","https://openalex.org/W2099471712","https://openalex.org/W2110052353","https://openalex.org/W2121313689","https://openalex.org/W2133564696","https://openalex.org/W2145039203","https://openalex.org/W2150010190","https://openalex.org/W2234895705","https://openalex.org/W2296704245","https://openalex.org/W2479932948","https://openalex.org/W2515954242","https://openalex.org/W2519887557","https://openalex.org/W2528639018","https://openalex.org/W2560675361","https://openalex.org/W2563738891","https://openalex.org/W2573587735","https://openalex.org/W2593182953","https://openalex.org/W2735643103","https://openalex.org/W2791999218","https://openalex.org/W2796303840","https://openalex.org/W2796640658","https://openalex.org/W2899911116","https://openalex.org/W2963125871","https://openalex.org/W2963358464","https://openalex.org/W2963373786","https://openalex.org/W2963440544","https://openalex.org/W2964015378","https://openalex.org/W2964308564","https://openalex.org/W4230994954","https://openalex.org/W4297772798","https://openalex.org/W4298157202","https://openalex.org/W4320013936","https://openalex.org/W6678186859","https://openalex.org/W6679434410","https://openalex.org/W6691096134","https://openalex.org/W6718379498","https://openalex.org/W6726873649","https://openalex.org/W6728547873","https://openalex.org/W6730235577","https://openalex.org/W6744582628","https://openalex.org/W6746015598","https://openalex.org/W6748850308","https://openalex.org/W6750642828","https://openalex.org/W6755906585"],"related_works":["https://openalex.org/W4293320219","https://openalex.org/W2953246223","https://openalex.org/W4283584549","https://openalex.org/W2554314924","https://openalex.org/W4288256692","https://openalex.org/W2998859928","https://openalex.org/W4381885966","https://openalex.org/W2969399009","https://openalex.org/W4398186750","https://openalex.org/W3151498616"],"abstract_inverted_index":{"Traffic":[0],"prediction":[1,20,133,156,175,181],"is":[2,48],"practically":[3],"important":[4],"to":[5,66,177,204,231],"facilitate":[6],"many":[7],"real":[8,255],"applications":[9],"in":[10,76,88,194,238,258],"urban":[11,40],"areas":[12],"such":[13],"as":[14,84,121,210],"relieving":[15],"traffic":[16,19,69,101,115,145,155,192,256],"congestion.":[17],"Traditional":[18],"models":[21],"are":[22,63],"mostly":[23],"statistic":[24,275],"based":[25,94,174,207,276],"methods,":[26],"and":[27,35,126,135,164,241,278],"they":[28,50,128],"cannot":[29],"effectively":[30],"capture":[31,217],"the":[32,39,68,73,98,114,131,141,179,185,191,211,218,222,259],"nonlinear,":[33],"stochastic":[34],"time-varying":[36],"characteristics":[37],"of":[38,45,71,81,117,143,213,225,243,263],"transportation":[41,79,227],"systems.":[42],"Another":[43],"limitation":[44],"these":[46],"methods":[47,95,112,277],"that":[49,269],"usually":[51],"focus":[52],"on":[53,140],"analyzing":[54],"one":[55],"or":[56,59],"several":[57],"roads":[58],"road":[60,74,99,119,223,261],"segments,":[61],"but":[62],"not":[64,137],"capable":[65],"predict":[67,190],"conditions":[70,193],"all":[72],"segments":[75,224],"a":[77,82,85,118,152,170,202,226,233,253],"large":[78,254],"network":[80,93,111,120,236,262],"city":[83],"whole.":[86],"Therefore,":[87],"recent":[89,279],"years,":[90],"deep":[91,109,154,281],"neural":[92,110],"for":[96,245],"forecasting":[97],"network-scale":[100,153],"have":[102],"been":[103],"emphasized":[104],"greatly.":[105],"However,":[106],"most":[107],"existing":[108],"model":[113,157,209],"data":[116],"\"images\"":[122],"rather":[123],"than":[124],"graphs,":[125],"thus":[127],"suffer":[129],"from":[130],"blurry":[132,180],"issue":[134,182],"do":[136],"perform":[138],"well":[139],"task":[142],"multi-step":[144],"prediction.":[146],"In":[147],"this":[148],"paper,":[149],"We":[150,248],"propose":[151,169,230],"called":[158],"GCGAN":[159,244,270],"by":[160,183],"combining":[161],"adversarial":[162,186],"training":[163,187],"graph":[165,234],"CNN.":[166],"Specifically,":[167],"we":[168,200,229],"Generative":[171],"Adversarial":[172],"Net":[173],"framework":[176],"address":[178],"introducing":[184],"loss.":[188],"To":[189,215],"multiple":[195],"future":[196],"time":[197],"intervals":[198],"simultaneously,":[199],"design":[201],"sequence":[203,205],"(Seq2Seq)":[206],"encoder-decoder":[208],"generator":[212,240],"GCGAN.":[214],"fully":[216],"spatial":[219],"correlations":[220],"among":[221],"network,":[228],"apply":[232],"convolution":[235],"(GCN)":[237],"both":[239,273],"discriminator":[242],"feature":[246],"learning.":[247],"evaluate":[249],"our":[250],"proposal":[251],"over":[252],"dataset":[257],"arterial":[260],"downtown":[264],"Chicago.":[265],"The":[266],"results":[267],"show":[268],"significantly":[271],"outperforms":[272],"traditional":[274],"state-of-the-art":[280],"learning":[282],"methods.":[283]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-06T13:50:29.536080","created_date":"2025-10-10T00:00:00"}
