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

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We propose a new approach to intelligent transportation systems for developing countries. Our system consists of two major components: (1) Web-camera-based traffic monitoring and (2) network flow estimation. The traffic monitoring module features a new algorithm for computing the vehicle count and velocity from very low-resolution webcam images. To reduce the cost of camera-wise collection of labeled (i.e. already counted) images, we develop a novel unsupervised learning approach. The network flow estimation module features a traffic flow estimation algorithm formalized as an inverse Markov chain problem, which finds the entire flow matrix from partial observations using an information-theoretic criterion. Using real webcams deployed in Nairobi, Kenya, we demonstrate the utility of our approach.

By: Tsuyoshi Idé, Takayuki Katsuki, Tetsuro Morimura, Robert Morris

Published in: IEEE Transactions on Intelligent Transportation Systems, volume 18, (no 4), pages 950-9; 10.1109/TITS.2016.2597160 in 2017


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