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keywords:(learning)

    MODELLING LEARNING AND ADAPTATION IN TRANSPORTATION CONTEXTS

    Arentze, Theo / Timmermans, Harry | Taylor & Francis Verlag | 2005
    Schlagwörter: learning and adaptation

    Wake distribution prediction on the propeller plane in ship design using artificial intelligence

    Kim, S.-Y. / Moon, B. Y. | Taylor & Francis Verlag | 2006
    Schlagwörter: learning algorithm

    Driver steering and muscle activity during a lane-change manoeuvre

    Pick, Andrew J. / Cole, David J. | Taylor & Francis Verlag | 2007
    Schlagwörter: Learning

    Short-term prediction of traffic dynamics with real-time recurrent learning algorithms

    Sheu, Jiuh-Biing / Lan, Lawrence W. / Huang, Yi-San | Taylor & Francis Verlag | 2009
    Schlagwörter: real-time recurrent learning

    Consumer learning behavior in choosing electric motorcycles

    Sung, Yen-Ching | Taylor & Francis Verlag | 2010
    Schlagwörter: Bayesian learning

    Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems

    Raksincharoensak, Pongsathorn / Khaisongkram, Wathanyoo / Nagai, Masao et al. | Taylor & Francis Verlag | 2010
    Schlagwörter: statistical machine learning

    On the potential for recognising of social interaction and social learning in modelling travellers’ change of behaviour under uncertainty

    Sunitiyoso, Yos / Avineri, Erel / Chatterjee, Kiron | Taylor & Francis Verlag | 2011
    Schlagwörter: social learning

    Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

    Freier Zugriff
    Munim, Ziaul Haque / Dushenko, Mariia / Jimenez, Veronica Jaramillo et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: machine learning

    Predicting incident duration using random forests

    Hamad, Khaled / Al-Ruzouq, Rami / Zeiada, Waleed et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: machine learning

    Collision-avoidance under COLREGS for unmanned surface vehicles via deep reinforcement learning

    Ma, Yong / Zhao, Yujiao / Wang, Yulong et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: deep reinforcement learning

    Identifying traffic conditions from non-traffic related sources

    Chamby-Diaz, Jorge C. / Estevam, Rhuam Sena / Bazzan, Ana L. C. | Taylor & Francis Verlag | 2020
    Schlagwörter: machine learning

    A machine learning-based method for simulation of ship speed profile in a complex ice field

    Freier Zugriff
    Milaković, Aleksandar-Saša / Li, Fang / Marouf, Mohamed et al. | Taylor & Francis Verlag | 2020
    Schlagwörter: machine 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

    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

    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

    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

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

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