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    Convolutional neural network for detecting railway fastener defects using a developed 3D laser system

    Zhan, You / Dai, Xianxing / Yang, Enhui et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    SATP-GAN: self-attention based generative adversarial network for traffic flow prediction

    Zhang, Liang / Wu, Jianqing / Shen, Jun et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: reinforcement learning

    A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow

    Parsa, Amir Bahador / Shabanpour, Ramin / Mohammadian, Abolfazl (Kouros) et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Calibrating microscopic traffic simulators using machine learning and particle swarm optimization

    Liu, Yanchen / Zou, Bo / Ni, Anning et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Application of machine learning algorithms in lane-changing model for intelligent vehicles exiting to off-ramp

    Dong, Changyin / Wang, Hao / Li, Ye et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Graph attention temporal convolutional network for traffic speed forecasting on road networks

    Zhang, Ke / He, Fang / Zhang, Zhengchao et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Designing a lightweight 1D convolutional neural network with Bayesian optimization for wheel flat detection using carbody accelerations

    Shi, Dachuan / Ye, Yunguang / Gillwald, Marco et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Traffic volume prediction on low-volume roadways: a Cubist approach

    Das, Subasish | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Incorporating congestion patterns into spatio-temporal deep learning algorithms

    Leiser, Neil / Yildirimoglu, Mehmet | Taylor & Francis Verlag | 2021
    Schlagwörter: machine learning

    Cruise dynamic pricing based on SARSA algorithm

    Wang, Jing / Yang, Dong / Chen, Kaimin et al. | Taylor & Francis Verlag | 2021
    Schlagwörter: reinforcement Learning

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