The comprehensive access to road traffic patterns in the continuously growing urban areas is key to achieve a sustainable mobility. However, the inherent complexity of urban traffic poses many challenges to achieve this goal, including: i) the need to integrate heterogeneous views of road traffic (such as speed limits, jam size, delay, throughput) from available sources; ii) the complex spatiotemporal intricacies of geolocalized speed and loop counter data; iii) the need to mine congestion patterns robust to the inherent traffic variability and unexpected occurrence of events, taking also into consideration the varying degrees of congestion severity; and iv) the need to guarantee the statistical significance and interpretability of the target patterns. In the context of our work, a road traffic pattern is a recurrent congestion profile (w.r.t. speed limits, jam extent and flow) that can span multiple locations and time periods within a day. Biclustering, the discovery of coherent subspaces (local patterns) within real-valued data, has unique properties of interest, being positioned to unravel such traffic patterns, while satisfying the aforementioned challenges. Despite its relevance, the potentialities of applying biclustering in mobility domains remain unexplored. This work proposes a structured view on why, when and how to apply biclustering for mining traffic patterns of road mobility, a subject remaining largely unexplored up to date. Using the city of Lisbon as a guiding case, we illustrate the relevance of biclustering geolocalized speed data and loop counter data. The gathered results confirm the role of biclustering in comprehensively finding statistically significant and actionable spatiotemporal associations of road mobility.


    Access

    Download


    Export, share and cite



    Title :

    Mining Actionable Patterns of Road Mobility From Heterogeneous Traffic Data Using Biclustering


    Contributors:


    Publication date :

    2022-07-01


    Size :

    3717993 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Traffic Risk Mining From Heterogeneous Road Statistics

    Moriya, Koichi / Matsushima, Shin / Yamanishi, Kenji | IEEE | 2018

    Free access


    From data to actionable knowledge and decision

    Sycara, K. / Lewis, M. | IEEE | 2002


    Scalable and Actionable Performance Measures for Traffic Signal Systems using Probe Vehicle Trajectory Data

    Waddell, Jonathan M. / Remias, Stephen M. / Kirsch, Jenna N. et al. | Transportation Research Record | 2020


    From Data to Actionable Knowledge and Decision

    Sycara, K. / Lewis, M. / International Society of Information Fusion et al. | British Library Conference Proceedings | 2002