41–60 von 120 Ergebnissen
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Ihre Suche:
keywords:(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

    Integrating data-driven and simulation models to predict traffic state affected by road incidents

    Shafiei, Sajjad / Mihăiţă, Adriana-Simona / Nguyen, Hoang et al. | Taylor & Francis Verlag | 2022
    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

    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

    Incorporating congestion patterns into spatio-temporal deep learning algorithms

    Leiser, Neil / Yildirimoglu, Mehmet | Taylor & Francis Verlag | 2021
    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

    Driver steering and muscle activity during a lane-change manoeuvre

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

    An autonomous location prediction model for maritime transport applications: a case study of Persian Gulf

    Khalilabadi, Mohammad Reza | Taylor & Francis Verlag | 2023
    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

    Arterial corridor travel time prediction under non-recurring conditions

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

    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

    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

    Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

    Zechin, Douglas / Cybis, Helena Beatriz Bettella | 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