Car hailing is undergoing rapid global development, thereby providing new opportunities and challenges to operators and transport engineers due to uneven or irregular demand in certain areas. To date, only a limited number of studies have analyzed regional mobility patterns or anomaly detection. This study therefore proposes a methodology for recognizing regional mobility patterns using car-hailing order datasets and point of interest datasets. More specifically, we detect regional mobility patterns by incorporating regional intrinsic properties to a hierarchical mixture model termed latent Dirichlet allocation (LDA). This model can simulate the process of generating car-hailing order data and yield regional mobility patterns from spatial, temporal, and spatiotemporal perspectives. Moreover, by combining the trained results with future mobility records, we can measure similarities between areas and detect anomalous areas by calculating the perplexity. We also implement our workflow on a real-word car-hailing order dataset and reveal that it is possible to identify areas with similar or anomaly mobility patterns. This research will contribute to the design of regional transportation policies and customized bus services.


    Access

    Download


    Export, share and cite



    Title :

    Understanding Regional Mobility Patterns Using Car-Hailing Order Data and Points of Interest Data


    Contributors:
    Zheng Zhang (author) / Yanyan Chen (author) / Jie Xiong (author) / Tianwen Liang (author)


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






    Spatio-temporal mobility patterns of on-demand ride-hailing service users

    Zhang, Jiechao / Hasan, Samiul / Yan, Xuedong et al. | Taylor & Francis Verlag | 2022


    HAILING SELF DRIVING PERSONAL MOBILITY DEVICES

    HAGHIGHAT KASHANI ALI / LEHMANN BASTIAN JAN MICHAEL / PLAICE SEAN TRACEY et al. | European Patent Office | 2020

    Free access

    Understanding distracted driving patterns of ride-hailing drivers from multi-source data: Applying association rule mining

    Xing, Guanyang / Chen, Shuyan / Ma, Yongfeng et al. | Taylor & Francis Verlag | 2024