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    Variational Autoencoder–Generative Adversarial Network Traffic Prediction with Ramps

    Source: Journal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 008::page 04026050-1
    Author:
    Wang, Feng
    ,
    Zhang, Yurui
    ,
    Sun, Tuo
    ,
    Hao, Ruochen
    DOI: 10.1061/JTEPBS.TEENG-9452
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAccurate traffic flow prediction is fundamental for effective traffic management and the optimization of highway systems, particularly in complex scenarios involving highway ramps. This paper proposes a novel deep-learning method that integrates ...
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      Variational Autoencoder–Generative Adversarial Network Traffic Prediction with Ramps

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4313731
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorWang, Feng
    contributor authorZhang, Yurui
    contributor authorSun, Tuo
    contributor authorHao, Ruochen
    date accessioned2026-08-20T20:57:32Z
    date available2026-08-20T20:57:32Z
    date copyright2026/05/25
    date issued2026
    identifier otherJTEPBS.TEENG-9452.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313731
    description abstractAbstractAccurate traffic flow prediction is fundamental for effective traffic management and the optimization of highway systems, particularly in complex scenarios involving highway ramps. This paper proposes a novel deep-learning method that integrates ...
    publisherAmerican Society of Civil Engineers
    titleVariational Autoencoder–Generative Adversarial Network Traffic Prediction with Ramps
    typeJournal Article
    journal volume152
    journal issue8
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-9452
    journal fristpage04026050-1
    journal lastpage04026050-9
    page9
    treeJournal of Transportation Engineering, Part A: Systems:;2026:;Volume ( 152 ):;issue: 008
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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