{"id":"https://openalex.org/W4416251087","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227328","title":"A Spatial-Temporal Interactive Graph Networks Fusion for Traffic Prediction","display_name":"A Spatial-Temporal Interactive Graph Networks Fusion for Traffic Prediction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251087","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227328"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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/A5069496916","display_name":"Xiang Yin","orcid":"https://orcid.org/0000-0002-6554-1516"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiang Yin","raw_affiliation_strings":["School of Software Henan University,Kaifeng,China,475000"],"affiliations":[{"raw_affiliation_string":"School of Software Henan University,Kaifeng,China,475000","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114388095","display_name":"Chengliang Xia","orcid":"https://orcid.org/0009-0009-6580-6295"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengliang Xia","raw_affiliation_strings":["School of Software Henan University,Kaifeng,China,475000"],"affiliations":[{"raw_affiliation_string":"School of Software Henan University,Kaifeng,China,475000","institution_ids":["https://openalex.org/I173899330"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5069496916"],"corresponding_institution_ids":["https://openalex.org/I173899330"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39103734,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"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":0.9832000136375427,"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":0.9832000136375427,"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":0.0024999999441206455,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.0013000000035390258,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6029999852180481},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.506600022315979},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.5013999938964844},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4465999901294708},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.364300012588501},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.3549000024795532},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3540000021457672}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7630000114440918},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6029999852180481},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.506600022315979},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47609999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44670000672340393},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4163999855518341},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3549000024795532},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.28049999475479126},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.25690001249313354},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2903871660","https://openalex.org/W2965341826","https://openalex.org/W3174022889","https://openalex.org/W3184564372","https://openalex.org/W4224293834","https://openalex.org/W4310896129","https://openalex.org/W4367301163","https://openalex.org/W4367595602","https://openalex.org/W4382239616","https://openalex.org/W4385296393","https://openalex.org/W4385654234","https://openalex.org/W4388343189","https://openalex.org/W4390547377","https://openalex.org/W4390905463","https://openalex.org/W4391935932","https://openalex.org/W4399601127","https://openalex.org/W4399850329","https://openalex.org/W4400062811","https://openalex.org/W4400146699","https://openalex.org/W4400390926"],"related_works":[],"abstract_inverted_index":{"Traffic":[0],"prediction":[1,29],"is":[2],"a":[3,12,76,103],"fundamental":[4],"technology":[5],"of":[6,15,69,130],"Intelligent":[7],"Transportation":[8],"Systems(ITS),":[9],"which":[10],"has":[11],"wide":[13],"range":[14],"applications.":[16],"However,":[17],"due":[18],"to":[19,61,85,108,126],"road":[20,55],"networks\u2019":[21],"complex":[22],"dynamic":[23,48],"spatial-temporal":[24,51,111],"dependencies,":[25],"achieving":[26],"high-precision":[27],"traffic":[28,114,134,142],"remains":[30],"challenging.":[31],"Despite":[32],"extensive":[33],"research":[34,59],"by":[35],"researchers,":[36],"two":[37],"pivotal":[38],"issues":[39],"persist.":[40],"First,":[41],"existing":[42],"models":[43],"do":[44],"not":[45],"fully":[46,62],"consider":[47],"and":[49,67,96,121],"implicit":[50],"relationships":[52],"within":[53],"the":[54,64,128,147],"network.":[56],"Second,":[57],"current":[58,152],"fails":[60],"capture":[63,110],"temporal":[65,131],"relationship":[66],"causality":[68],"time":[70],"series":[71],"data.":[72,115,135],"This":[73,89],"paper":[74],"proposes":[75],"novel":[77],"model":[78,90,149],"called":[79],"Spatial-Temporal":[80,104],"Interactive":[81,105],"Graph":[82,93,98],"Networks":[83],"Fusion(STIGNF)":[84],"address":[86],"these":[87],"issues.":[88],"combines":[91],"Adaptive":[92],"Convolutional":[94,99,123],"Network(AGCN)":[95],"Dynamic":[97],"Network(DGCN)":[100],"modules":[101],"through":[102],"Learning":[106],"Strategy(STILS)":[107],"better":[109],"correlations":[112],"in":[113,133],"Additionally,":[116],"it":[117],"employs":[118],"Transform":[119],"Attention(TA)":[120],"Temporal":[122],"Neural":[124],"Network(TCNN)":[125],"enhance":[127],"extraction":[129],"features":[132],"Experiments":[136],"on":[137],"four":[138],"publicly":[139],"available":[140],"real-world":[141],"flow":[143],"datasets":[144],"demonstrate":[145],"that":[146],"STIGNF":[148],"significantly":[150],"outperforms":[151],"state-of-the-art":[153],"baseline":[154],"models\u2019":[155],"overall":[156],"predictive":[157],"accuracy.":[158]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}