As increasing volumes of urban data are being available, new opportunities arise for data-driven analysis that can lead to improvements in the lives of citizens through evidence-based policies. In particular, taxi trip is an important urban sensor that provides unprecedented insights into many aspects of a city, from economic activity, human mobility to land development. However, analyzing these data presents many challenges, e.g., sparse data for fine-grained patterns, and the regularity submerged by seemingly random data. Inspired by above challenges, we focus on Pick-Up (PU)/Drop-Off (DO) points from taxi trips, and propose a fine-grained approach to unveil a set of low spatio-temporal patterns from the regularity-discovered intensity. The proposed method is conceptually simple yet efficient, by leveraging point process to handle sparsity of points, and by decomposing point intensities into the low-rank regularity and the factorized basis patterns, our approach enables domain experts to discover patterns that are previously unattainable for them, from a case study motivated by traffic engineers.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Discovering Fine-Grained Spatial Pattern From Taxi Trips: Where Point Process Meets Matrix Decomposition and Factorization


    Contributors:
    Pang, Junbiao (author) / Huang, Jing (author) / Yang, Xue (author) / Wang, Zuyun (author) / Yu, Haitao (author) / Huang, Qingming (author) / Yin, Baocai (author)


    Publication date :

    2018-10-01


    Size :

    9230606 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Spatiotemporal Pattern Analysis of Taxi Trips in New York City

    Hochmair, Hartwig H. | Transportation Research Record | 2016




    Matrix Factorization for Globally Consistent Periodic Flow Prediction in Taxi Systems

    Forouzandeh Jonaghani, Rouzbeh / Wachowicz, Monica / Hanson, Trevor | Transportation Research Record | 2023

    Free access