ThroughCompressive analysisSensing on real-world mobility data, we observe non-trivial traffic correlations among the traffic conditions of different road segments and derive a mathematical model to capture such relations. After mathematical manipulation, the models can be used to construct representation bases to sparsely represent the traffic conditions of all road segments. With the trait of sparse representation, we propose a traffic monitoring approach that applies the compressiveCompressive sensingSensing technique to achieve city-scale traffic estimations with only a small number of probe vehicles, largely reducing the system operating cost. Trace-driven experiments with real-world traffic data show that the proposed approach derives accurate traffic conditions with the average accuracy as 80%, based on only 50 probe vehicles’ intervention.


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    Title :

    A Compressive Sensing Based Traffic Monitoring Approach


    Additional title:

    SpringerBriefs Computer Sci.


    Contributors:
    Liu, Zhidan (author) / Wu, Kaishun (author)


    Publication date :

    2021-05-19


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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