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

We present a new approach to lightweight intelligent transportation systems. Our 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 t...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2017-04, Vol.18 (4), p.950-959
Main Authors: Ide, Tsuyoshi, Katsuki, Takayuki, Morimura, Tetsuro, Morris, Robert
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Data acquisition
Estimation
Gaussian mixtures
image analysis
Image quality
Inference algorithms
Intelligent transportation systems
inverse Markov problem
Machine learning
Machine learning algorithms
Markov chains
Monitoring
object counting
Roads
Traffic flow
Traffic information
Transportation networks
variational Bayes
Vehicles
Webcams
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Summary:We present a new approach to lightweight intelligent transportation systems. Our 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. Our 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. We evaluated our approach on two different traffic data sets, one acquired in Nairobi, Kenya, and the other in Kyoto, Japan.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2597160