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    Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

    Source: Journal of Transportation Engineering, Part A: Systems:;2020:;Volume ( 146 ):;issue: 008
    Author:
    Wan Li
    ,
    Xuegang “Jeff” Ban
    ,
    Jianfeng Zheng
    ,
    Henry X. Liu
    ,
    Cheng Gong
    ,
    Yong Li
    DOI: 10.1061/JTEPBS.0000384
    Publisher: ASCE
    Abstract: The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.
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      Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4268111
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorWan Li
    contributor authorXuegang “Jeff” Ban
    contributor authorJianfeng Zheng
    contributor authorHenry X. Liu
    contributor authorCheng Gong
    contributor authorYong Li
    date accessioned2022-01-30T21:23:25Z
    date available2022-01-30T21:23:25Z
    date issued8/1/2020 12:00:00 AM
    identifier otherJTEPBS.0000384.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4268111
    description abstractThe traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.
    publisherASCE
    titleReal-Time Movement-Based Traffic Volume Prediction at Signalized Intersections
    typeJournal Paper
    journal volume146
    journal issue8
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.0000384
    page15
    treeJournal of Transportation Engineering, Part A: Systems:;2020:;Volume ( 146 ):;issue: 008
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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