@article{ref1,
title="Traffic velocity estimation from vehicle count sequences",
journal="IEEE transactions on intelligent transportation systems",
year="2017",
author="Katsuki, Takayuki and Morimura, Tetsuro and Inoue, Masato",
volume="18",
number="7",
pages="1700-1712",
abstract="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. The authors 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. The authors also derive a sampling-based algorithm for the density estimation. The authors show the effectiveness of their method on artificial and real-world data sets.<p /><p>Language: en</p>",
language="en",
issn="1524-9050",
doi="10.1109/TITS.2016.2628384",
url="http://dx.doi.org/10.1109/TITS.2016.2628384"
}