{"id":"https://openalex.org/W4416222440","doi":"https://doi.org/10.1109/tits.2025.3628554","title":"Dynamic Spatiotemporal Graph Convolutional Neural Network Based on Congestion Propagation for Traffic Prediction","display_name":"Dynamic Spatiotemporal Graph Convolutional Neural Network Based on Congestion Propagation for Traffic Prediction","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W4416222440","doi":"https://doi.org/10.1109/tits.2025.3628554"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3628554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3628554","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,"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"},"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/A5103476650","display_name":"Wenxie Lin","orcid":"https://orcid.org/0009-0003-6962-1577"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenxie Lin","raw_affiliation_strings":["School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhe Zhang","orcid":"https://orcid.org/0000-0003-2465-3914"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Zhang","raw_affiliation_strings":["School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114244602","display_name":"Ruiyu Wang","orcid":"https://orcid.org/0009-0008-7672-970X"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiyu Wang","raw_affiliation_strings":["School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004466915","display_name":"Gang Ren","orcid":"https://orcid.org/0000-0002-3412-0831"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gang Ren","raw_affiliation_strings":["School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032581092","display_name":"Yanyan Zhao","orcid":"https://orcid.org/0000-0002-1428-5022"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangzhen Zhao","raw_affiliation_strings":["School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053196577","display_name":"Jianhua Song","orcid":"https://orcid.org/0000-0003-1132-9672"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Song","raw_affiliation_strings":["School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101692229","display_name":"Qi Cao","orcid":"https://orcid.org/0000-0003-3242-8071"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Cao","raw_affiliation_strings":["School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation, Southeast University, Jiulonghu Campus, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5103476650"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":0.9575,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.80743034,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"27","issue":"1","first_page":"1079","last_page":"1092"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9667999744415283,"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":0.9667999744415283,"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":0.006800000090152025,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.00279999990016222,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/interpretability","display_name":"Interpretability","score":0.6459000110626221},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.6209999918937683},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5809000134468079},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5454000234603882},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.4699999988079071},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4392000138759613},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4138999879360199},{"id":"https://openalex.org/keywords/graph-theory","display_name":"Graph theory","score":0.3840999901294708}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404999732971191},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6459000110626221},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.6209999918937683},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5809000134468079},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5454000234603882},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4138999879360199},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C195563490","wikidata":"https://www.wikidata.org/wiki/Q180368","display_name":"Network congestion","level":3,"score":0.3765000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37229999899864197},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3571000099182129},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C76444178","wikidata":"https://www.wikidata.org/wiki/Q72897900","display_name":"Connectivity","level":3,"score":0.2867000102996826},{"id":"https://openalex.org/C64093975","wikidata":"https://www.wikidata.org/wiki/Q356677","display_name":"Floating car data","level":3,"score":0.2831999957561493},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2660999894142151},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3628554","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3628554","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,"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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1095835165","display_name":null,"funder_award_id":"52221005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4498633348","display_name":null,"funder_award_id":"2022M710679","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4717280714","display_name":null,"funder_award_id":"52202399","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4875800412","display_name":null,"funder_award_id":"52072068","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1973943669","https://openalex.org/W1983883318","https://openalex.org/W1984969638","https://openalex.org/W2145039203","https://openalex.org/W2164863800","https://openalex.org/W2606202972","https://openalex.org/W2613322775","https://openalex.org/W2740759433","https://openalex.org/W2900448968","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2904832339","https://openalex.org/W2949732208","https://openalex.org/W2955819484","https://openalex.org/W2965341826","https://openalex.org/W2966398094","https://openalex.org/W2991205212","https://openalex.org/W2996847713","https://openalex.org/W2998559444","https://openalex.org/W3081203761","https://openalex.org/W3165322078","https://openalex.org/W3209485964","https://openalex.org/W4210497946","https://openalex.org/W4211213044","https://openalex.org/W4230512065","https://openalex.org/W4288060068","https://openalex.org/W4293558662","https://openalex.org/W4308080775","https://openalex.org/W4312703862","https://openalex.org/W4313594206","https://openalex.org/W4313855753","https://openalex.org/W4322755838","https://openalex.org/W4379116774","https://openalex.org/W4385271883","https://openalex.org/W4390603637","https://openalex.org/W4390604340","https://openalex.org/W4391093686","https://openalex.org/W4395702745","https://openalex.org/W4399669397","https://openalex.org/W4400142828","https://openalex.org/W4400766720","https://openalex.org/W4404117982","https://openalex.org/W4404177755","https://openalex.org/W4404222313","https://openalex.org/W4405310974"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"traffic":[1,47,72,75,82,127,145],"prediction":[2],"is":[3,98,106],"crucial":[4],"for":[5,143],"the":[6,30,43,69,80,101,109,154],"management":[7],"of":[8,71,126,158],"intelligent":[9],"transportation":[10],"systems.":[11],"Recently,":[12],"researchers":[13],"have":[14],"developed":[15],"spatiotemporal":[16,61,132,140],"graph":[17,31,51,62,84,119],"neural":[18,120],"networks":[19],"(ST-GNNs),":[20],"achieving":[21],"significant":[22],"progress":[23],"in":[24],"performance.":[25],"However,":[26],"most":[27],"ST-GNNs":[28],"construct":[29],"adjacency":[32,112],"matrix":[33,94,105,113],"using":[34],"predefined":[35],"rules":[36],"or":[37],"trainable":[38],"parameters,":[39],"without":[40],"fully":[41],"leveraging":[42],"congestion":[44,73,87,92,103],"relationships":[45],"within":[46],"flows":[48],"to":[49,114],"guide":[50],"structure":[52],"learning.":[53],"Addressing":[54],"this":[55],"issue,":[56],"we":[57],"propose":[58],"a":[59,91,116,137],"dynamic":[60,118,124],"convolutional":[63,141],"network":[64,121],"(DSTGCN),":[65],"which":[66],"adequately":[67],"considers":[68],"impact":[70],"on":[74,79,150],"prediction.":[76,146],"Firstly,":[77],"based":[78],"fundamental":[81],"flow":[83],"model":[85],"and":[86,156],"propagation":[88,93,104],"path":[89],"algorithm,":[90],"among":[95],"road":[96,111],"nodes":[97],"constructed.":[99],"Then,":[100],"real-time":[102],"fused":[107],"with":[108],"static":[110],"establish":[115],"time-varying":[117],"structure,":[122],"enabling":[123],"capture":[125],"state":[128],"changes.":[129],"Finally,":[130],"complex":[131],"correlations":[133],"are":[134],"captured":[135],"through":[136],"specially":[138],"designed":[139],"architecture":[142],"multi-horizon":[144],"Extensive":[147],"experiments":[148],"conducted":[149],"real-world":[151],"datasets":[152],"demonstrate":[153],"effectiveness":[155],"interpretability":[157],"our":[159],"approach.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-12T00:00:00"}