{"id":"https://openalex.org/W4416250201","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228829","title":"Dynamic Multi-Scale Spatial-Temporal Feature Network for Traffic Flow Forecasting","display_name":"Dynamic Multi-Scale Spatial-Temporal Feature Network for Traffic Flow Forecasting","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250201","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228829"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228829","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228829","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":"conference-paper","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/A5053863886","display_name":"Daming Liu","orcid":"https://orcid.org/0000-0002-1774-0721"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Daming Liu","raw_affiliation_strings":["Shanghai University of Electric Power,College of Computer Science and Technology,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University of Electric Power,College of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I23632641"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100683869","display_name":"Chunlin Wang","orcid":"https://orcid.org/0000-0003-4675-1355"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunlin Wang","raw_affiliation_strings":["Shanghai University of Electric Power,College of Computer Science and Technology,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai University of Electric Power,College of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I23632641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I23632641"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.4619615,"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":0.9851999878883362,"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":0.9851999878883362,"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":0.003800000064074993,"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":0.002400000113993883,"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":0.6420999765396118},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.5688999891281128},{"id":"https://openalex.org/keywords/control-flow-graph","display_name":"Control flow graph","score":0.47589999437332153},{"id":"https://openalex.org/keywords/flow-network","display_name":"Flow network","score":0.47049999237060547},{"id":"https://openalex.org/keywords/traffic-generation-model","display_name":"Traffic generation model","score":0.45339998602867126},{"id":"https://openalex.org/keywords/dynamic-network-analysis","display_name":"Dynamic network analysis","score":0.4401000142097473},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4278999865055084},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.34769999980926514},{"id":"https://openalex.org/keywords/network-traffic-simulation","display_name":"Network traffic simulation","score":0.3440000116825104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7524999976158142},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6420999765396118},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6085000038146973},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.5688999891281128},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.47589999437332153},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.47049999237060547},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C13540734","wikidata":"https://www.wikidata.org/wiki/Q5318996","display_name":"Dynamic network analysis","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4278999865055084},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C94168897","wikidata":"https://www.wikidata.org/wiki/Q574324","display_name":"Network traffic simulation","level":4,"score":0.3440000116825104},{"id":"https://openalex.org/C197298091","wikidata":"https://www.wikidata.org/wiki/Q5318963","display_name":"Dynamic data","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3043999969959259},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C2988166257","wikidata":"https://www.wikidata.org/wiki/Q924286","display_name":"Traffic network","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2685999870300293},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26649999618530273},{"id":"https://openalex.org/C2988224531","wikidata":"https://www.wikidata.org/wiki/Q20830730","display_name":"Network structure","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.26100000739097595},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C489000","wikidata":"https://www.wikidata.org/wiki/Q747385","display_name":"Data flow diagram","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C34947359","wikidata":"https://www.wikidata.org/wiki/Q665189","display_name":"Complex network","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228829","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228829","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2047332899","https://openalex.org/W2345702419","https://openalex.org/W2756203131","https://openalex.org/W2807894308","https://openalex.org/W2901504064","https://openalex.org/W2903871660","https://openalex.org/W2904832339","https://openalex.org/W2944924828","https://openalex.org/W2965341826","https://openalex.org/W2972752351","https://openalex.org/W2996847713","https://openalex.org/W3004515714","https://openalex.org/W3027983943","https://openalex.org/W3123909522","https://openalex.org/W3126367810","https://openalex.org/W3158304688","https://openalex.org/W3171958173","https://openalex.org/W4289533938","https://openalex.org/W4382239616","https://openalex.org/W4382449675","https://openalex.org/W4387846860","https://openalex.org/W4388343189","https://openalex.org/W4404235734","https://openalex.org/W4404905428"],"related_works":[],"abstract_inverted_index":{"Traffic":[0],"flow":[1,29,71,90,135,152,189],"forecasting":[2],"has":[3],"always":[4],"been":[5,18],"one":[6],"of":[7,168,177],"the":[8,23,32,44,82,101,107,116,174,192],"popular":[9],"research":[10],"topics":[11],"in":[12,20,27,123],"intelligent":[13],"transportation,":[14],"but":[15,113,204],"challenges":[16],"have":[17],"encountered":[19],"effectively":[21],"capturing":[22],"intricate":[24],"spatial-temporal":[25,46,118,128,145,169,178],"dependencies":[26],"traffic":[28,70,89,124,134,151,188],"data,":[30],"and":[31,95,143,191],"graph":[33,76,83,102],"structures":[34],"utilized":[35],"by":[36,49,121],"existing":[37],"methods":[38],"fail":[39],"to":[40,53,103],"adequately":[41],"account":[42],"for":[43,69],"unique":[45],"patterns":[47],"exhibited":[48],"each":[50],"node":[51],"due":[52],"time-varying":[54],"spatiality.":[55],"To":[56],"address":[57],"these":[58,164],"issues,":[59],"a":[60,74,138,155],"Dynamic":[61,156],"Multi-Scale":[62],"Spatial-Temporal":[63,139],"Feature":[64],"Network":[65,159],"(DMSTFN)":[66],"is":[67,78],"proposed":[68],"forecasting.":[72],"Specifically,":[73],"dynamic":[75,117],"constructor":[77],"designed,":[79],"which":[80],"updates":[81],"structure":[84],"based":[85],"on":[86,185],"real-time":[87],"input":[88],"data":[91,136,153],"during":[92],"both":[93],"training":[94],"testing":[96],"phases,":[97],"this":[98],"approach":[99,184],"allows":[100],"not":[104,198],"only":[105,199],"reflect":[106],"static":[108],"geographical":[109],"proximity":[110],"among":[111],"nodes":[112],"also":[114,205],"capture":[115],"relationships":[119],"caused":[120],"changes":[122],"patterns.":[125],"Moreover,":[126],"long-term":[127],"features":[129,146],"are":[130,147],"extracted":[131,148],"from":[132,149],"long-input":[133],"using":[137],"Encoder-Decoder":[140],"(STED)":[141],"architecture,":[142],"short-term":[144],"short-input":[150],"utilizing":[154],"Graph":[157],"Convolutional":[158],"(DGCN).":[160],"By":[161],"efficiently":[162],"fusing":[163],"two":[165],"different":[166],"scales":[167],"features,":[170],"our":[171,183],"method":[172],"addresses":[173],"complex":[175],"issue":[176],"dependencies.":[179],"Finally,":[180],"we":[181],"evaluate":[182],"four":[186],"public":[187],"datasets,":[190],"experimental":[193],"results":[194],"demonstrate":[195],"that":[196],"DMSTFN":[197],"exhibits":[200],"high":[201],"computational":[202],"efficiency":[203],"outperforms":[206],"12":[207],"state-of-the-art":[208],"baselines.":[209]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-11-14T00:00:00"}
