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1–20 von 29 Ergebnissen
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    Network–wide prediction of public transportation ridership using spatio–temporal link–level information

    Karnberger, Stephan / Antoniou, Constantinos | Elsevier | 2019
    Schlagwörter: Machine learning

    Bridges across borders: A clustering approach to support EU regional policy

    Christodoulou, Aris / Christidis, Panayotis | Elsevier | 2020
    Schlagwörter: Machine learning

    Combatting the mismatch: Modeling bike-sharing rental and return machine learning classification forecast in Seoul, South Korea

    Choi, Seung Jun / Jiao, Junfeng / Lee, Hye Kyung et al. | Elsevier | 2023
    Schlagwörter: Machine learning

    Using machine learning for direct demand modeling of ridesourcing services in Chicago

    Yan, Xiang / Liu, Xinyu / Zhao, Xilei | Elsevier | 2020
    Schlagwörter: Machine learning

    Exploring the non-linear associations between spatial attributes and walking distance to transit

    Tao, Tao / Wang, Jueyu / Cao, Xinyu | Elsevier | 2019
    Schlagwörter: Machine learning

    Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning

    Zhang, Xiaojian / Zhou, Zhengze / Xu, Yiming et al. | Elsevier | 2023
    Schlagwörter: Explainable machine learning

    Examining the non-linear effects of transit accessibility on daily trip duration: A focus on the low-income population

    Tao, Sui / Cheng, Long / He, Sylvia et al. | Elsevier | 2023
    Schlagwörter: Machine-learning

    Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning

    Zhou, Xiaolu / Wang, Mingshu / Li, Dongying | Elsevier | 2019
    Schlagwörter: Machine learning

    To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults

    Yang, Linchuan / Ao, Yibin / Ke, Jintao et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    A methodology to develop a geospatial transportation typology

    Popovich, Natalie / Spurlock, C. Anna / Needell, Zachary et al. | Elsevier | 2021
    Schlagwörter: Machine learning

    Exploring the contributions of Ebike ownership, transit access, and the built environment to car ownership in a developing city

    Sun, Shan / Guo, Liang / Yang, Shuo et al. | Elsevier | 2024
    Schlagwörter: Machine learning

    Classification of automobile and transit trips from Smartphone data: Enhancing accuracy using spatial statistics and GIS

    Nour, Akram / Hellinga, Bruce / Casello, Jeffrey | Elsevier | 2015
    Schlagwörter: Machine learning

    An origin-destination level analysis on the competitiveness of bike-sharing to underground using explainable machine learning

    Lv, Huitao / Li, Haojie / Chen, Yanlu et al. | Elsevier | 2023
    Schlagwörter: Machine learning

    Data-driven interpretation on interactive and nonlinear effects of the correlated built environment on shared mobility

    Gao, Kun / Yang, Ying / Gil, Jorge et al. | Elsevier | 2023
    Schlagwörter: Interpretable machine learning

    Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms

    Yousefzadeh Barri, Elnaz / Farber, Steven / Jahanshahi, Hadi et al. | Elsevier | 2022
    Schlagwörter: Machine learning

    A kilometer or a mile? Does buffer size matter when it comes to car ownership?

    Laviolette, Jérôme / Morency, Catherine / Waygood, E.O.D. | Elsevier | 2022
    Schlagwörter: Machine learning

    Detecting anomalous commuting patterns: Mismatch between urban land attractiveness and commuting activities

    Tong, Zhaomin / Zhang, Ziyi / An, Rui et al. | Elsevier | 2024
    Schlagwörter: Machine learning model

    Machine learning approach for spatial modeling of ridesourcing demand

    Zhang, Xiaojian / Zhao, Xilei | Elsevier | 2022
    Schlagwörter: Machine learning

    Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach

    Ashik, F.R. / Sreezon, A.I.Z. / Rahman, M.H. et al. | Elsevier | 2024
    Schlagwörter: Machine learning