{"id":"https://openalex.org/W4390905463","doi":"https://doi.org/10.1109/tits.2024.3349565","title":"PGCN: Progressive Graph Convolutional Networks for Spatial\u2013Temporal Traffic Forecasting","display_name":"PGCN: Progressive Graph Convolutional Networks for Spatial\u2013Temporal Traffic Forecasting","publication_year":2024,"publication_date":"2024-01-16","ids":{"openalex":"https://openalex.org/W4390905463","doi":"https://doi.org/10.1109/tits.2024.3349565"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3349565","doi":"https://doi.org/10.1109/tits.2024.3349565","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3349565","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,"listed_in":["abdc-a","anvur-scientific","ccf-b","cwts-core","dongbi-a","jufo-2","ki-jl-1","norway-1","poland-140","russia-white-list-1","vabb-shw"],"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"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2202.08982","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079331220","display_name":"Yuyol Shin","orcid":"https://orcid.org/0000-0002-1445-0112","observed_orcids":["https://orcid.org/0000-0002-1445-0112"]},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yuyol Shin","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-1445-0112","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047381304","display_name":"Yoonjin Yoon","orcid":"https://orcid.org/0000-0002-3550-4431","observed_orcids":["https://orcid.org/0000-0002-3550-4431"]},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yoonjin Yoon","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-3550-4431","affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":{"value":2495,"currency":"USD","value_usd":2495},"apc_paid":null,"fwci":29.8319,"has_fulltext":true,"cited_by_count":166,"citation_normalized_percentile":{"value":0.99943985,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":"25","issue":"7","first_page":"7633","last_page":"7644"},"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/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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":1.0,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/traffic-forecasting","display_name":"traffic prediction","score":0.9700000286102295},{"id":"https://openalex.org/keywords/graph-convolutional-networks","display_name":"graph convolutional networks","score":0.8040000200271606},{"id":"https://openalex.org/keywords/spatiotemporal-correlation","display_name":"spatio-temporal correlation","score":0.7749999761581421},{"id":"https://openalex.org/keywords/transportation-networks","display_name":"transportation networks","score":0.6489999890327454},{"id":"https://openalex.org/keywords/dilated-causal-convolution","display_name":"dilated causal convolution","score":0.8980000019073486},{"id":"https://openalex.org/keywords/skip-connections","display_name":"skip connections","score":0.5649999976158142}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7300448417663574},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5564946532249451},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4911629855632782},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42489778995513916},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.42402857542037964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.423501580953598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38186633586883545},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35778117179870605},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tits.2024.3349565","doi":"https://doi.org/10.1109/tits.2024.3349565","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3349565","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,"listed_in":["abdc-a","anvur-scientific","ccf-b","cwts-core","dongbi-a","jufo-2","ki-jl-1","norway-1","poland-140","russia-white-list-1","vabb-shw"],"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"},{"id":"pmh:oai:arXiv.org:2202.08982","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.08982","pdf_url":"https://arxiv.org/pdf/2202.08982","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2202.08982","doi":"https://doi.org/10.48550/arxiv.2202.08982","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2202.08982","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2202.08982","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2202.08982","pdf_url":"https://arxiv.org/pdf/2202.08982","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[{"score":0.761900007724762,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"},{"score":0.7207000255584717,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G3026822327","display_name":null,"funder_award_id":"2020R1A2C2010200","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G8156294967","display_name":null,"funder_award_id":"2021R1A4A1033486","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G8656845458","display_name":null,"funder_award_id":"2020R1A2C2010200","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390905463.pdf","grobid_xml":"https://content.openalex.org/works/W4390905463.grobid-xml"},"referenced_works_count":49,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W2004353783","https://openalex.org/W2031674781","https://openalex.org/W2069929199","https://openalex.org/W2073640212","https://openalex.org/W2085592822","https://openalex.org/W2468907370","https://openalex.org/W2519091744","https://openalex.org/W2519887557","https://openalex.org/W2559655401","https://openalex.org/W2579495707","https://openalex.org/W2606780347","https://openalex.org/W2612690371","https://openalex.org/W2624431344","https://openalex.org/W2626778328","https://openalex.org/W2756203131","https://openalex.org/W2766453196","https://openalex.org/W2768008502","https://openalex.org/W2792839479","https://openalex.org/W2793820729","https://openalex.org/W2805516822","https://openalex.org/W2899457523","https://openalex.org/W2903871660","https://openalex.org/W2904832339","https://openalex.org/W2945622688","https://openalex.org/W2948246283","https://openalex.org/W2963636093","https://openalex.org/W2965341826","https://openalex.org/W2972752351","https://openalex.org/W2990138404","https://openalex.org/W2996451395","https://openalex.org/W2996847713","https://openalex.org/W2997848713","https://openalex.org/W3004515714","https://openalex.org/W3034749137","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3080344546","https://openalex.org/W3126367810","https://openalex.org/W3157286395","https://openalex.org/W3163053496","https://openalex.org/W3171958173","https://openalex.org/W3213655484","https://openalex.org/W4240592325","https://openalex.org/W4252337780","https://openalex.org/W4313039183","https://openalex.org/W4353093955","https://openalex.org/W6794315554","https://openalex.org/W7133205715"],"related_works":["https://openalex.org/W4416249991","https://openalex.org/W4380989535","https://openalex.org/W3201545219","https://openalex.org/W4399247349","https://openalex.org/W3038981236","https://openalex.org/W3212041461","https://openalex.org/W3215209860","https://openalex.org/W4320149722","https://openalex.org/W4385578078","https://openalex.org/W3213215274"],"abstract_inverted_index":{"The":[0],"complex":[1],"spatial-temporal":[2],"correlations":[3],"in":[4,78,92,195,216],"transportation":[5,14],"networks":[6],"make":[7],"the":[8,35,42,52,58,65,69,82,93,126,134,149,154,173,187,201,212],"traffic":[9,79,83,103,174,181],"forecasting":[10,80,104],"problem":[11],"challenging.":[12],"Since":[13],"system":[15],"inherently":[16],"possesses":[17],"graph":[18,27,49,53,146],"structures,":[19],"many":[20],"research":[21],"efforts":[22],"have":[23],"been":[24],"put":[25],"with":[26,153,193,220],"neural":[28],"networks.":[29],"Recently,":[30],"constructing":[31],"adaptive":[32],"graphs":[33,117],"to":[34,121,136,162,178,205,208,214],"data":[36,66,84,124,210],"has":[37],"shown":[38],"promising":[39],"results":[40],"over":[41],"models":[43],"relying":[44],"on":[45],"a":[46,101,114],"single":[47],"static":[48],"structure.":[50],"However,":[51],"adaptations":[54],"are":[55],"applied":[56,177],"during":[57,68,125],"training":[59,127],"phases":[60],"and":[61,90,128,158,168],"do":[62],"not":[63],"reflect":[64],"used":[67],"testing":[70,129],"phases.":[71,130],"Such":[72],"shortcomings":[73],"can":[74],"be":[75],"problematic":[76],"especially":[77],"since":[81],"often":[85],"suffer":[86],"from":[87],"unexpected":[88],"changes":[89],"irregularities":[91],"time":[94],"series.":[95],"In":[96],"this":[97],"study,":[98],"we":[99,132],"propose":[100],"novel":[102],"framework":[105],"called":[106],"Progressive":[107],"Graph":[108],"Convolutional":[109],"Network":[110],"(PGCN).":[111],"PGCN":[112,171,204],"constructs":[113],"set":[115],"of":[116,183,203],"by":[118,141],"progressively":[119,206],"adapting":[120],"online":[122],"input":[123,209],"Specifically,":[131],"implemented":[133],"model":[135,150,189,213],"construct":[137],"progressive":[138],"adjacency":[139],"matrices":[140],"learning":[142],"trend":[143],"similarities":[144],"among":[145],"nodes.":[147],"Then,":[148],"is":[151],"combined":[152],"dilated":[155],"causal":[156],"convolution":[157],"gated":[159],"activation":[160],"unit":[161],"extract":[163],"temporal":[164],"features.":[165],"With":[166],"residual":[167],"skip":[169],"connections,":[170],"performs":[172],"prediction.":[175],"When":[176],"seven":[179],"real-world":[180],"datasets":[182],"diverse":[184],"geometric":[185],"nature,":[186],"proposed":[188],"achieves":[190],"state-of-the-art":[191],"performance":[192],"consistency":[194],"all":[196],"datasets.":[197],"We":[198],"conclude":[199],"that":[200],"ability":[202],"adapt":[207],"enables":[211],"generalize":[215],"different":[217],"study":[218],"sites":[219],"robustness.":[221]},"counts_by_year":[{"year":2026,"cited_by_count":71},{"year":2025,"cited_by_count":67},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2024-01-17T00:00:00"}