Traffic Velocity Estimation from Vehicle Count Sequences

Takayuki Katsuki, Tetsuro Morimura, Masato Inoue

Research output: Contribution to journalArticlepeer-review

4 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


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

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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