{"id":"https://openalex.org/W7154487057","doi":"https://doi.org/10.1016/j.neunet.2026.108989","title":"DSTFGCN: A dynamic spatial-temporal fusion graph convolution network for traffic flow forecasting","display_name":"DSTFGCN: A dynamic spatial-temporal fusion graph convolution network for traffic flow forecasting","publication_year":2026,"publication_date":"2026-04-15","ids":{"openalex":"https://openalex.org/W7154487057","doi":"https://doi.org/10.1016/j.neunet.2026.108989","pmid":"https://pubmed.ncbi.nlm.nih.gov/42001625"},"language":"en","primary_location":{"id":"doi:10.1016/j.neunet.2026.108989","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.108989","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.neunet.2026.108989","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114230184","display_name":"Tianyi Pan","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyi Pan","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 2023223040073@stu.scu.edu.cn"],"raw_orcid":"https://orcid.org/0009-0006-1367-6204","affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 2023223040073@stu.scu.edu.cn","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinyuan Zhou","orcid":"https://orcid.org/0009-0007-3837-0663"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyuan Zhou","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 2024323040016@stu.scu.edu.cn"],"raw_orcid":"https://orcid.org/0009-0007-3837-0663","affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 2024323040016@stu.scu.edu.cn","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032662894","display_name":"Shiyong Lan","orcid":"https://orcid.org/0000-0002-7109-9170"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shiyong Lan","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China. Electronic address: lanshiyong@scu.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China. Electronic address: lanshiyong@scu.edu.cn","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133702172","display_name":"Wenwu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Wenwu Wang","raw_affiliation_strings":["Department of Electrical and Electronic Engineering, University of Surrey, Guildford, UK. Electronic address: w.wang@surrey.ac.uk"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, University of Surrey, Guildford, UK. Electronic address: w.wang@surrey.ac.uk","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133657459","display_name":"Hongyu Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyu Yang","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 363879736@qq.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 363879736@qq.com","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133653216","display_name":"Zheng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Li","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 1041897080@qq.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 1041897080@qq.com","institution_ids":["https://openalex.org/I24185976"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133727638","display_name":"Zhiang Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiang Hou","raw_affiliation_strings":["National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China. Electronic address: 2023226045008@stu.scu.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China. Electronic address: 2023226045008@stu.scu.edu.cn","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133691977","display_name":"Yao Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Ren","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 2023223045140@stu.scu.edu.cn"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China. Electronic address: 2023223045140@stu.scu.edu.cn","institution_ids":["https://openalex.org/I24185976"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5032662894"],"corresponding_institution_ids":["https://openalex.org/I24185976"],"apc_list":{"value":3350,"currency":"USD","value_usd":3350},"apc_paid":{"value":3350,"currency":"USD","value_usd":3350},"fwci":6.4383,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.96116847,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"201","issue":null,"first_page":"108989","last_page":"108989"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9901000261306763,"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":0.9901000261306763,"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":0.0012000000569969416,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.0006000000284984708,"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/graph","display_name":"Graph","score":0.5315999984741211},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5198000073432922},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4542999863624573},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4246000051498413},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3781000077724457},{"id":"https://openalex.org/keywords/traffic-network","display_name":"Traffic network","score":0.3555999994277954},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34689998626708984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6970000267028809},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5198000073432922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5072000026702881},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4625000059604645},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4542999863624573},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C2988166257","wikidata":"https://www.wikidata.org/wiki/Q924286","display_name":"Traffic network","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3280999958515167},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27239999175071716},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.26019999384880066}],"mesh":[{"descriptor_ui":"D000098422","descriptor_name":"Graph Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098422","descriptor_name":"Graph Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098422","descriptor_name":"Graph Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D005544","descriptor_name":"Forecasting","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1016/j.neunet.2026.108989","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.108989","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},{"id":"pmid:42001625","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42001625","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural networks : the official journal of the International Neural Network Society","raw_type":null}],"best_oa_location":{"id":"doi:10.1016/j.neunet.2026.108989","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.neunet.2026.108989","pdf_url":null,"source":{"id":"https://openalex.org/S123019304","display_name":"Neural Networks","issn_l":"0893-6080","issn":["0893-6080","1879-2782"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Networks","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1970998544","display_name":null,"funder_award_id":"62371324","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322990","display_name":"Sichuan University","ror":"https://ror.org/011ashp19"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1963826206","https://openalex.org/W1983883318","https://openalex.org/W2046033161","https://openalex.org/W2069929199","https://openalex.org/W2111991989","https://openalex.org/W2613331518","https://openalex.org/W2751760169","https://openalex.org/W3006854884","https://openalex.org/W3082935815","https://openalex.org/W3126367810","https://openalex.org/W3193812480","https://openalex.org/W3210458411","https://openalex.org/W4210497946","https://openalex.org/W4318317809","https://openalex.org/W4367299897","https://openalex.org/W4367595602","https://openalex.org/W4376851236","https://openalex.org/W4385296393","https://openalex.org/W4386918844","https://openalex.org/W4389515566","https://openalex.org/W4399884308","https://openalex.org/W4400142828","https://openalex.org/W4401115067","https://openalex.org/W4401359865","https://openalex.org/W4404177755","https://openalex.org/W4404838660","https://openalex.org/W4404905428","https://openalex.org/W4405586497","https://openalex.org/W4410153049","https://openalex.org/W4410813166","https://openalex.org/W4411040652","https://openalex.org/W4412634018","https://openalex.org/W4413185215","https://openalex.org/W4413765231"],"related_works":[],"abstract_inverted_index":{"Traffic":[0],"flow":[1],"prediction":[2],"is":[3,16],"one":[4],"of":[5,9,35,147],"the":[6,20,33,78,99,110,120,128,145,148],"core":[7],"technology":[8],"Intelligent":[10],"Transportation":[11],"System.":[12],"Its":[13],"fundamental":[14],"challenge":[15],"to":[17,108,118,124],"effectively":[18],"model":[19],"complex":[21],"spatial-temporal":[22,92],"dependencies.":[23,62,158],"Although":[24],"extensive":[25],"research":[26],"has":[27],"been":[28],"conducted":[29],"in":[30,41],"this":[31],"field,":[32],"limitations":[34],"current":[36,169],"methods":[37,48,67],"restrict":[38],"their":[39],"effectiveness":[40],"accurate":[42],"predictions.":[43],"For":[44,63],"temporal":[45,100,115],"dependence,":[46],"existing":[47,66],"based":[49,143],"on":[50,56,144,160],"recurrent":[51],"neural":[52],"networks":[53],"only":[54],"focus":[55],"local":[57,111,154],"dependencies":[58,112,122],"and":[59,113,151,155],"ignore":[60],"global":[61,121,156],"spatial":[64,129,157],"dependencies,":[65],"use":[68],"predefined":[69],"or":[70],"adaptive":[71],"adjacency":[72],"matrices":[73],"that":[74,166],"cannot":[75],"accurately":[76],"reflect":[77],"relationships":[79],"between":[80],"real":[81],"traffic":[82],"flow.":[83],"To":[84],"overcome":[85],"these":[86],"limitations,":[87],"we":[88,102,131],"propose":[89,132],"a":[90,133],"dynamic":[91,134,141],"fusion":[93],"graph":[94,116,135],"convolution":[95,107,117,136],"network":[96],"(DSTFGCN).":[97],"In":[98,127],"aspect,":[101,130],"introduce":[103],"gated":[104],"dilated":[105],"causal":[106],"capture":[109,119],"node-independent":[114],"specific":[123],"each":[125],"node.":[126],"block.":[137],"It":[138],"can":[139],"construct":[140],"graphs":[142],"characteristics":[146],"input":[149],"data":[150],"aggregate":[152],"both":[153],"Experiments":[159],"six":[161],"real-world":[162],"datasets":[163],"have":[164],"shown":[165],"DSTFGCN":[167],"outperforms":[168],"mainstream":[170],"methods.":[171],"The":[172],"codes":[173],"are":[174],"available":[175],"at":[176],"https://github.com/SYLan2019/DSTFGCN.":[177]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2026-04-16T00:00:00"}
