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81–100 von 204 Ergebnissen
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    Small-amplitude bogie hunting identification method for high-speed trains based on machine learning

    Guo, Jinying / Zhang, Gexiang / Shi, Huailong et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Assessing influential factors for lane change behavior using full real-world vehicle-by-vehicle data

    Basso, Franco / Cifuentes, Álvaro / Cuevas-Pavincich, Francisca et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: Interpretable Machine Learning

    Predicting incident duration using random forests

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

    Driver steering and muscle activity during a lane-change manoeuvre

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

    Equality of public transit connectivity: the influence of mass rapid transit services on individual buildings for Singapore

    Li, Zengxiang / Ren, Shen / Hu, Nan et al. | Taylor & Francis Verlag | 2019
    Schlagwörter: machine learning

    Friction-adaptive stochastic nonlinear model predictive control for autonomous vehicles

    Vaskov, Sean / Quirynen, Rien / Menner, Marcel et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    EasyJet pricing strategy: determinants and developments

    Malighetti, Paolo / Paleari, Stefano / Redondi, Renato | Taylor & Francis Verlag | 2015
    Schlagwörter: demand learning

    DLW-Net model for traffic flow prediction under adverse weather

    Yao, Ronghan / Zhang, Wensong / Long, Meng | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning

    Prediction of pedestrians’ wait-or-go decision using trajectory data based on gradient boosting decision tree

    Xin, Xiuying / Jia, Ning / Ling, Shuai et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: ensemble 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

    Arterial corridor travel time prediction under non-recurring conditions

    Shafiei, Sajjad / Wang, Eileen / Grzybowska, Hanna et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    Characterizing parking systems from sensor data through a data-driven approach

    Arjona Martinez, Jamie / Linares, Maria Paz / Casanovas, Josep | Taylor & Francis Verlag | 2021
    Schlagwörter: deep learning

    Vehicle yaw stability control with a two-layered learning MPC

    Zhang, Zhiming / Xie, Lei / Lu, Shan et al. | Taylor & Francis Verlag | 2023
    Schlagwörter: model learning

    Estimating cycle-level real-time traffic movements at signalized intersections

    Mahmoud, Nada / Abdel-Aty, Mohamed / Cai, Qing et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: machine learning

    A machine learning based optimisation method to evaluate the crushing behaviours of square tubes with rectangular-hole-type initiators

    Liang, Rui / Xu, Fengxiang / Liu, Na et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: Machine learning

    Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

    Zechin, Douglas / Cybis, Helena Beatriz Bettella | Taylor & Francis Verlag | 2023
    Schlagwörter: machine learning

    In-store shopping trip predictions and impact factors during COVID-19 emergencies

    Imran, Md Ashraful / Hyun, Kate | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Analyzing multi-factor effects on travel well-being, including non-linear relationship and interaction

    Yu, Hongmei / Ye, Xiaofei / Liu, Lining et al. | Taylor & Francis Verlag | 2024
    Schlagwörter: machine learning

    Development of a novel engine power model to estimate heavy-duty truck fuel consumption

    Kan, Yuheng / Liu, Hao / Lu, Xiaoyun et al. | Taylor & Francis Verlag | 2022
    Schlagwörter: deep learning