City-Wide Traffic Flow Estimation From a Limited Number of Low-Quality Cameras

The authors present a new approach to lightweight intelligent transportation systems. Their approach does not rely on traditional expensive infrastructures, but rather on advanced machine learning algorithms. It takes images from traffic cameras at a limited number of locations and estimates the traffic over the entire road network. The authors' approach features two main algorithms. The first is a probabilistic vehicle counting algorithm from low-quality images that falls into the category of unsupervised learning. The other is a network inference algorithm based on an inverse Markov chain formulation that infers the traffic at arbitrary links from a limited number of observations. The authors evaluated their approach on two different traffic data sets, one acquired in Nairobi, Kenya, and the other in Kyoto, Japan.

Language

  • English

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Filing Info

  • Accession Number: 01634026
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Mar 30 2017 3:56PM