{"id":"https://openalex.org/W4410608942","doi":"https://doi.org/10.1109/tvt.2025.3572622","title":"STGCNFormer: Spatio-Temporal Dual-Stream Graph Convolutional Networks and Transformers for Traffic Forecasting","display_name":"STGCNFormer: Spatio-Temporal Dual-Stream Graph Convolutional Networks and Transformers for Traffic Forecasting","publication_year":2025,"publication_date":"2025-05-22","ids":{"openalex":"https://openalex.org/W4410608942","doi":"https://doi.org/10.1109/tvt.2025.3572622"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2025.3572622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3572622","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","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/A5074191109","display_name":"Changzhi Yang","orcid":"https://orcid.org/0000-0002-3908-1309"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changzhi Yang","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China"],"affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053317950","display_name":"Huihui Pan","orcid":"https://orcid.org/0000-0002-8931-1774"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huihui Pan","raw_affiliation_strings":["Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China"],"affiliations":[{"raw_affiliation_string":"Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100440638","display_name":"Jue Wang","orcid":"https://orcid.org/0009-0003-8461-8827"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jue Wang","raw_affiliation_strings":["Ningbo Institute of Intelligent Equipment Technology Company Ltd., Ningbo, China"],"affiliations":[{"raw_affiliation_string":"Ningbo Institute of Intelligent Equipment Technology Company Ltd., Ningbo, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074191109"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":1.0422,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.76051233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"74","issue":"10","first_page":"15254","last_page":"15263"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9860000014305115,"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":0.9860000014305115,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9811999797821045,"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/T10799","display_name":"Data Visualization and Analytics","score":0.9456999897956848,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5587185025215149},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5202659368515015},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.498687744140625},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4356740713119507},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2623364329338074},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.18973183631896973},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.18253010511398315},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.165745347738266}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5587185025215149},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5202659368515015},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.498687744140625},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4356740713119507},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2623364329338074},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.18973183631896973},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.18253010511398315},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.165745347738266},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2025.3572622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2025.3572622","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"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 Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W793439335","https://openalex.org/W2047332899","https://openalex.org/W2158787690","https://openalex.org/W2293786206","https://openalex.org/W2342662179","https://openalex.org/W2756203131","https://openalex.org/W2808862972","https://openalex.org/W2903871660","https://openalex.org/W2965341826","https://openalex.org/W2997848713","https://openalex.org/W3025388795","https://openalex.org/W3034749137","https://openalex.org/W3080253043","https://openalex.org/W3151130473","https://openalex.org/W3160694286","https://openalex.org/W3190492058","https://openalex.org/W4234722734","https://openalex.org/W4285145831","https://openalex.org/W4291910369","https://openalex.org/W4312529005","https://openalex.org/W4312688875","https://openalex.org/W4312898211","https://openalex.org/W4313168581","https://openalex.org/W4313546915","https://openalex.org/W4320015890","https://openalex.org/W4362689842","https://openalex.org/W4382449675","https://openalex.org/W4385245566","https://openalex.org/W4391407136","https://openalex.org/W4396240786","https://openalex.org/W4399563381","https://openalex.org/W4401300880","https://openalex.org/W4401878963","https://openalex.org/W4406457520"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"In":[0,67],"intelligent":[1],"transportation":[2],"systems,":[3],"traffic":[4,11,17,84],"prediction":[5,205],"constitutes":[6],"the":[7,119,129,134,195],"fundamental":[8],"component":[9,112],"of":[10,101,121,128,155,175],"optimization,":[12],"aiming":[13],"to":[14,61,132],"precisely":[15],"forecast":[16],"flow.":[18],"Modeling":[19],"spatio-temporal":[20,43,87],"interactions":[21,139,161],"is":[22,65,103,113],"a":[23,58,80,92,96,104,114,147,152,170,176,181,188],"crucial":[24],"step":[25],"in":[26,89],"achieving":[27,141],"long-term":[28],"and":[29,50,77,95,124,140,162,180],"accurate":[30],"predictions.":[31],"Traditional":[32],"methods":[33],"are":[34,52],"mostly":[35],"based":[36],"on":[37],"single-stream":[38,59],"networks,":[39],"which":[40,117,158,185],"alternately":[41],"extract":[42],"features":[44,88],"through":[45,91],"sequential":[46],"iterations.":[47],"However,":[48],"time":[49],"space":[51],"inherently":[53],"two":[54],"different":[55],"modalities.":[56],"Employing":[57],"network":[60,82],"address":[62],"multi-modal":[63],"tasks":[64],"suboptimal.":[66],"this":[68],"paper,":[69],"we":[70,145,168],"propose":[71,169],"Spatio-Temporal":[72],"Dual-Stream":[73],"Graph":[74],"Convolutional":[75],"Networks":[76],"Transformers":[78],"(STGCNFormer),":[79],"dual-stream":[81],"for":[83,191],"forecasting,":[85],"decoupling":[86],"parallel":[90],"temporal":[93,160,177],"stream":[94],"spatial":[97,138,182],"stream.":[98],"The":[99,110],"core":[100],"STGCNFormer":[102,105,202],"block,":[106,116,149],"comprising":[107],"three":[108],"components.":[109],"first":[111,130],"GCN":[115],"restricts":[118],"kernel":[120],"graph":[122],"convolution":[123],"adopts":[125],"Chebyshev":[126],"polynomials":[127],"kind":[131],"approximate":[133],"convolutional":[135],"kernel,":[136],"capturing":[137],"localized":[142],"convolution.":[143],"Secondly,":[144],"present":[146],"Transformer":[148],"designed":[150],"as":[151,187],"simplified":[153],"version":[154],"vanilla":[156],"Transformers,":[157],"captures":[159],"significantly":[163],"reduces":[164],"computational":[165,209],"complexity.":[166],"Thirdly,":[167],"cross":[171],"attention":[172,178,183],"mechanism,":[173,184],"consisting":[174],"mechanism":[179],"serves":[186],"bidirectional":[189],"bridge":[190],"sharing":[192],"information":[193],"between":[194],"dual":[196],"streams.":[197],"Experimental":[198],"results":[199],"demonstrate":[200],"that":[201],"achieves":[203],"superior":[204],"accuracy":[206],"with":[207,212],"reduced":[208],"complexity":[210],"compared":[211],"previous":[213],"state-of-the-art":[214],"methods.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
