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dc.contributor.authorShen, Boyan
dc.contributor.authorWang, Zhiwen
dc.contributor.authorLing, Guobi
dc.contributor.authorWang, Haoxu
dc.contributor.authorCheng, Xiaolong
dc.contributor.authorMiao, Wei
dc.date.accessioned2026-08-20T20:55:58Z
dc.date.available2026-08-20T20:55:58Z
dc.date.copyright2025/11/03
dc.date.issued2026
dc.identifier.otherJTEPBS.TEENG-9240.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313691
dc.description.abstractAbstractIn intelligent transport systems (ITS), accurate and real-time traffic flow prediction is essential to reduce congestion and manage transport. Numerous existing studies have used hybrid models to extract various intrinsic patterns of traffic flow ...Practical ApplicationsIn intelligent transportation management, accurately predicting road traffic flow is crucial for alleviating congestion and optimizing road networks. Traditional methods are easily disrupted by noise in the data and struggle to fully ...
dc.publisherAmerican Society of Civil Engineers
dc.titleMFMA–Informer: A Short-Term Traffic Flow Prediction Model Incorporating Spatiotemporal Features
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue1
dc.journal.titleJournal of Transportation Engineering, Part A: Systems
dc.identifier.doi10.1061/JTEPBS.TEENG-9240
dc.journal.fristpage04025117-1
dc.journal.lastpage04025117-13
dc.page13
dc.treeJournal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 001
dc.contenttypeFulltext


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