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    Traffic speed prediction for intelligent transportation system based on a deep feature fusion model

    Li, Linchao / Qu, Xu / Zhang, Jian et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: deep learning , machine learning

    Design of Reinforcement Learning Parameters for Seamless Application of Adaptive Traffic Signal Control

    El-Tantawy, Samah / Abdulhai, Baher / Abdelgawad, Hossam | Taylor & Francis Verlag | 2014
    Schlagwörter: Reinforcement Learning , Temporal Difference Learning

    Using reinforcement learning to minimize taxi idle times

    O’Keeffe, Kevin / Anklesaria, Sam / Santi, Paolo et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning , reinforcement learning

    Learning through policy transfer? Reviewing a decade of scholarship for the field of transport

    Freier Zugriff
    Glaser, Meredith / Bertolini, Luca / te Brömmelstroet, Marco et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: policy learning , learning

    GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model

    Guo, Jingqiu / Liu, Yangzexi / Yang, Qingyan (Ken) et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach

    Cheng, Yanqiu / Hu, Xianbiao / Chen, Kuanmin et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep Q-learning , reinforcement learning

    Few-Shot traffic prediction based on transferring prior knowledge from local network

    Yu, Lin / Guo, Fangce / Sivakumar, Aruna et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Few-shot learning , Transfer learning

    Deep reinforcement learning in dynamic positioning control: by rewarding small response of riser angles

    Wang, Fang / Bai, Yong / Bai, Jie et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Reinforcement learning , Q-learning

    A cold-start-free reinforcement learning approach for traffic signal control

    Xiao, Nan / Yu, Liang / Yu, Jinqiang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning , reinforcement learning

    Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data

    Wang, Hao / Yuan, Yun / Yang, Xianfeng Terry et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep reinforcement learning , Q-learning

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