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    MFMA–Informer: A Short-Term Traffic Flow Prediction Model Incorporating Spatiotemporal Features

    Source: Journal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 001::page 04025117-1
    Author:
    Shen, Boyan
    ,
    Wang, Zhiwen
    ,
    Ling, Guobi
    ,
    Wang, Haoxu
    ,
    Cheng, Xiaolong
    ,
    Miao, Wei
    DOI: 10.1061/JTEPBS.TEENG-9240
    Publisher: American Society of Civil Engineers
    Abstract: AbstractIn 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 ...
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      MFMA–Informer: A Short-Term Traffic Flow Prediction Model Incorporating Spatiotemporal Features

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313691
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorShen, Boyan
    contributor authorWang, Zhiwen
    contributor authorLing, Guobi
    contributor authorWang, Haoxu
    contributor authorCheng, Xiaolong
    contributor authorMiao, Wei
    date accessioned2026-08-20T20:55:58Z
    date available2026-08-20T20:55:58Z
    date copyright2025/11/03
    date issued2026
    identifier otherJTEPBS.TEENG-9240.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313691
    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 ...
    publisherAmerican Society of Civil Engineers
    titleMFMA–Informer: A Short-Term Traffic Flow Prediction Model Incorporating Spatiotemporal Features
    typeJournal Article
    journal volume152
    journal issue1
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-9240
    journal fristpage04025117-1
    journal lastpage04025117-13
    page13
    treeJournal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 001
    contenttypeFulltext
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