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keywords:(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

    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

    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

    Condition assessment of high-speed railway track structure based on sparse Bayesian extreme learning machine and Bayesian hypothesis testing

    Wang, Senrong / Gao, Jingze / Lin, Chao et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: sparse bayesian learning , extreme learning machine

    The level of delay caused by crashes (LDC) in metropolitan and non-metropolitan areas: a comparative analysis of improved Random Forests and LightGBM

    Wang, Zehao / Jiao, Pengpeng / Wang, Jianyu et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: feature learning , machine learning

    An evacuation guidance model for pedestrians with limited vision

    Han, Yanbin / Liu, Hong / Li, Liang | Taylor & Francis Verlag | 2023
    Schlagwörter: reinforcement learning

    Eco-driving at signalized intersections: a parameterized reinforcement learning approach

    Jiang, Xia / Zhang, Jian / Li, Dan | Taylor & Francis Verlag | 2023
    Schlagwörter: reinforcement learning

    Application of mayfly algorithm for prediction of removed sediment in hydro-suction dredging systems

    Mahdavi-Meymand, Amin / Zounemat-Kermani, Mohammad | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Leveraging autonomous vehicles in mixed-autonomy traffic networks with reinforcement learning-controlled intersections

    Mosharafian, Sahand / Afzali, Shirin / Mohammadpour Velni, Javad | Taylor & Francis Verlag | 2023
    Schlagwörter: Reinforcement learning

    Social group detection based on multi-level consistent behaviour characteristics

    Li, Meng / Chen, Tao / Du, Hao et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: unsupervised learning

    DRL-based adaptive signal control for bus priority service under connected vehicle environment

    Zhang, Xinshao / He, Zhaocheng / Zhu, Yiting et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: deep reinforcement learning

    Integration of machine learning and statistical models for crash frequency modeling

    Zhou, Dongqin / Gayah, Vikash V. / Wood, Jonathan S. | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning

    Estimating the effect of biofouling on ship shaft power based on sensor measurements

    Freier Zugriff
    Bakka, Haakon / Rognebakke, Hanne / Glad, Ingrid et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Prediction of extent of damage in vehicle during crash using improved XGBoost model

    Vadhwani, Diya / Thakor, Devendra | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Method for automated detection of outliers in crash simulations

    Kracker, David / Dhanasekaran, Revan Kumar / Schumacher, Axel et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    An interpretable machine learning framework to understand bikeshare demand before and during the COVID-19 pandemic in New York City

    Uddin, Majbah / Hwang, Ho-Ling / Hasnine, Md Sami | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis

    Freier Zugriff
    Rawson, Andrew / Brito, Mario | Taylor & Francis Verlag | 2023
    Schlagwörter: Machine learning