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    Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios

    Pereira, Francisco C. / Rodrigues, Filipe / Ben-Akiva, Moshe | Taylor & Francis Verlag | 2015
    Keywords: Data mining , Web Mining

    Understanding the usage of dockless bike sharing in Singapore

    Shen, Yu / Zhang, Xiaohu / Zhao, Jinhua | Taylor & Francis Verlag | 2018
    Keywords: data mining

    Understanding the bike sharing travel demand and cycle lane network: The case of Shanghai

    Zhuang, Dingyi / Jin, Jian Gang / Shen, Yifan et al. | Taylor & Francis Verlag | 2021
    Keywords: data mining

    Tyre-force estimation by Kalman inverse filtering: applications to off-highway mining trucks

    Siegrist, P. M. / Mcaree, P. R. | Taylor & Francis Verlag | 2006
    Keywords: Mining truck dynamics

    Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data

    Chang, Ximing / Wu, Jianjun / Liu, Hao et al. | Taylor & Francis Verlag | 2019
    Keywords: data mining

    Temporal pattern mining of urban traffic volume data: a pairwise hybrid clustering method

    Free access
    Taheri Sarteshnizi, Iman / Sarvi, Majid / Bagloee, Saeed Asadi et al. | Taylor & Francis Verlag | 2023
    Keywords: Temporal pattern mining

    Solid–liquid two-phase flow in deep-sea mining pipelines based on CFD–DEM

    Li, Yuanwen / Liu, Shaojun / Hu, Xiaozhou | Taylor & Francis Verlag | 2023
    Keywords: Deep-sea mining pipeline

    Severity prediction of motorcycle crashes with machine learning methods

    Wahab, Lukuman / Jiang, Haobin | Taylor & Francis Verlag | 2020
    Keywords: data mining

    Risk assessment of inland waterborne transportation using data mining

    Wang, Zhaochen / Yin, Jingbo | Taylor & Francis Verlag | 2020
    Keywords: Text mining , association rule mining

    Predicting future locations of moving objects with deep fuzzy-LSTM networks

    Li, Mingxiao / Lu, Feng / Zhang, Hengcai et al. | Taylor & Francis Verlag | 2020
    Keywords: trajectory data mining