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keywords:(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

    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 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

    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

    Performance evaluation of mode choice models under balanced and imbalanced data assumptions

    Rezaei, Shahrbanoo / Khojandi, Anahita / Haque, Antora Mohsena et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: imbalanced learning , machine learning

    Shipping market forecasting by forecast combination mechanism

    Gao, Ruobin / Liu, Jiahui / Du, Liang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    Detecting transportation modes using smartphone data and GIS information: evaluating alternative algorithms for an integrated smartphone-based travel diary imputation

    Liu, Yicong / Miller, Eric / Habib, Khandker Nurul | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning , tree-based ensemble learning

    Microscopic modeling of cyclists on off-street paths: a stochastic imitation learning approach

    Mohammed, Hossameldin / Sayed, Tarek / Bigazzi, Alexander | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    A data-driven lane-changing behavior detection system based on sequence learning

    Gao, Jun / Murphey, Yi Lu / Yi, Jiangang et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: sequence 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

    Testing and enhancing spatial transferability of artificial neural networks based travel behavior models

    Koushik, Anil NP / Manoj, M / Nezamuddin, N et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning , Transfer learning

    A bibliometric analysis and review on reinforcement learning for transportation applications

    Li, Can / Bai, Lei / Yao, Lina et al. | Taylor & Francis Verlag | 2023
    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

    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

    Station-level short-term demand forecast of carsharing system via station-embedding-based hybrid neural network

    Zhao, Feifei / Wang, Weiping / Sun, Huijun et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: Machine learning