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    An iterative learning approach for anticipatory traffic signal control on urban networks

    Huang, Wei / Viti, Francesco / Tampère, Chris M.J. | Taylor & Francis Verlag | 2017
    Schlagwörter: iterative learning

    The influence of alternative data smoothing prediction techniques on the performance of a two-stage short-term urban travel time prediction framework

    Guo, Fangce / Krishnan, Rajesh / Polak, John | Taylor & Francis Verlag | 2017
    Schlagwörter: machine learning method

    Data-driven train set crash dynamics simulation

    Tang, Zhao / Zhu, Yunrui / Nie, Yinyu et al. | Taylor & Francis Verlag | 2017
    Schlagwörter: machine learning

    Road accident data analysis using Bayesian networks

    Karimnezhad, Ali / Moradi, Fahimeh | Taylor & Francis Verlag | 2017
    Schlagwörter: Structure learning

    A multi-agent learning approach for online calibration and consistency checking of real-time traffic network management systems

    Hashemi, Hossein / Abdelghany, Khaled F. / Abdelghany, Ahmed F. | Taylor & Francis Verlag | 2017
    Schlagwörter: multi-agent learning

    Artificial intelligence for traffic signal control based solely on video images

    Jeon, Hyunjeong / Lee, Jincheol / Sohn, Keemin | Taylor & Francis Verlag | 2018
    Schlagwörter: deep learning , reinforcement learning (RL)

    A data-driven dynamics simulation framework for railway vehicles

    Nie, Yinyu / Tang, Zhao / Liu, Fengjia et al. | Taylor & Francis Verlag | 2018
    Schlagwörter: machine learning

    Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

    Aziz, H. M. Abdul / Zhu, Feng / Ukkusuri, Satish V. | Taylor & Francis Verlag | 2018
    Schlagwörter: reinforcement learning

    Inferring passenger types from commuter eigentravel matrices

    Legara, Erika Fille T. / Monterola, Christopher P. | Taylor & Francis Verlag | 2018
    Schlagwörter: machine learning

    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

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