Traffic Velocity Estimation from Vehicle Count Sequences

Takayuki Katsuki, Tetsuro Morimura, Masato Inoue

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)


    Traffic velocity is a fundamental metric for inferring traffic conditions. This paper proposes a new velocity estimation approach from temporal sequences of vehicle count that does not require tracking any vehicles or using any labeled data. It is useful for measuring traffic velocities with low quality and inexpensive sensors such as web cameras in general use. We formalize the task as a density estimation problem by introducing a new model for temporal sequences of vehicle counts wherein the correlation between the sequences is directly related to the traffic velocity. We also derive a sampling-based algorithm for the density estimation. We show the effectiveness of our method on artificial and real-world data sets.

    Original languageEnglish
    Article number7782816
    Pages (from-to)1700-1712
    Number of pages13
    JournalIEEE Transactions on Intelligent Transportation Systems
    Issue number7
    Publication statusPublished - 2017 Jul 1


    • Bayes procedures
    • Intelligent transportation systems
    • unsupervised learning
    • velocity measurement

    ASJC Scopus subject areas

    • Automotive Engineering
    • Mechanical Engineering
    • Computer Science Applications


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