دانلود مقاله انگلیسی رایگان:توسعه چارچوب شبیه سازی حرکت جمعی مردم مبتنی بر یادگیری تقویتی - 2020
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  • Development of people mass movement simulation framework based on reinforcement learning Development of people mass movement simulation framework based on reinforcement learning
    Development of people mass movement simulation framework based on reinforcement learning

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Development of people mass movement simulation framework based on reinforcement learning


    ترجمه فارسی عنوان مقاله:

    توسعه چارچوب شبیه سازی حرکت جمعی مردم مبتنی بر یادگیری تقویتی


    منبع:

    Sciencedirect - Elsevier - Transportation Research Part C, 117 (2020) 102706. doi:10.1016/j.trc.2020.102706


    نویسنده:

    Yanbo Panga,⁎, Takehiro Kashiyamaa, Takahiro Yabea,b, Kota Tsubouchic, Yoshihide Sekimotoa


    چکیده انگلیسی:

    Understanding individual and crowd dynamics in urban environments is critical for numerous applications, such as urban planning, traffic forecasting, and location-based services. However, researchers have developed travel demand models to accomplish this task with survey data that are expensive and acquired at low frequencies. In contrast, emerging data collection methods have enabled researchers to leverage machine learning techniques with a tremendous amount of mobility data for analyzing and forecasting people’s behaviors. In this study, we developed a reinforcement learning-based approach for modeling and simulation of people mass movement using the global positioning system (GPS) data. Unlike traditional travel demand modeling approaches, our method focuses on the problem of inferring the spatio-temporal preferences of individuals from the observed trajectories, and is based on inverse reinforcement learning (IRL) techniques. We applied the model to the data collected from a smartphone application and attempted to replicate a large amount of the population’s daily movement by incorporating with agent-based multi-modal traffic simulation technologies. The simulation results indicate that agents can successfully learn and generate human-like travel activities. Furthermore, the proposed model performance significantly outperforms the existing methods in synthetic urban dynamics.
    Keywords: Travel demand modeling | Reinforcement learning | Mobility data | Citywide people mass movement simulation


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 16
    حجم فایل: 6243 کیلوبایت

    قیمت: رایگان


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