دانلود مقاله انگلیسی رایگان:یادگیری عمیق یکپارچه و مدل تعقیب خودرو تصادفی برای پویایی ترافیک در بزرگراه های چند خطه - 2019
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  • Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways
    Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways


    ترجمه فارسی عنوان مقاله:

    یادگیری عمیق یکپارچه و مدل تعقیب خودرو تصادفی برای پویایی ترافیک در بزرگراه های چند خطه


    منبع:

    Sciencedirect - Elsevier - Transportation Research Part C, 106 (2019) 360-377: doi:10:1016/j:trc:2019:07:023


    نویسنده:

    Seunghyeon Leea,⁎, Dong Ngoduyb,⁎, Mehdi Keyvan-Ekbatanib


    چکیده انگلیسی:

    The current paper proposes a novel stochastic procedure for modelling car-following behaviours on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following model where a deep learning architecture is used to estimate a probability of lanechanging (LC) manoeuvres. To the best of our knowledge, this work is among the very few papers which exploit deep learning to model driving behaviour on a multi-lane road. The objective of this study is to establish a coupled stochastic continuous multi-lane car-following model using Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore, imaged second-based trajectories of the lane-changer and surrounding vehicles are used to identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated using a real-world high-resolution vehicle trajectory dataset. The results indicate that the prediction of the integrated SOVM is almost identical to the observed trajectories of the lanechangers and the following vehicles in the initial and the target lane. It has been found that the proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone.
    Keywords: Stochastic car-following model | Deep learning | Lane-changing behaviour


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 18
    حجم فایل: 7277 کیلوبایت

    قیمت: رایگان


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