@article{ref1,
title="Identifying crash type propensity using real-time traffic data on freeways",
journal="Journal of safety research",
year="2011",
author="Christoforou, Zoi and Cohen, Simon and Karlaftis, Matthew G.",
volume="42",
number="1",
pages="43-50",
abstract="Introduction: We examine the effects of various traffic parameters on type of road crash. Method: Multivariate probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Results: Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Rear-end crashes involving two vehicles were found to be more probable for relatively low values of both speed and density, rear-end crashes involving more than two vehicles appear to be more probable under congested conditions, while single-vehicle crashes appear to be largely geometry-dependent. Impact on Industry: Results could be integrated in a real-time traffic management application.<p /> <p>Language: en</p>",
language="en",
issn="0022-4375",
doi="10.1016/j.jsr.2011.01.001",
url="http://dx.doi.org/10.1016/j.jsr.2011.01.001"
}