@article{ref1,
title="Incorporating speed in a traffic conflict severity index to estimate left turn opposed crashes at signalized intersections",
journal="Transportation research record",
year="2021",
author="Anarkooli, Alireza Jafari and Persaud, Bhagwant and Milligan, Craig and Penner, Joel and Saleem, Taha",
volume="2675",
number="5",
pages="214-225",
abstract="Rigorous evaluation of implemented safety treatments, especially for innovative treatments and those targeted at rare crash types, is challenging to accomplish with conventional crash-based analyses. This paper aims to address this challenge for treatments at urban signalized intersections by providing a methodology that uses surrogate measures of safety obtained from video analytics to predict changes in crashes. To develop this approach, left turn opposed traffic conflicts based on post-encroachment times, along with corresponding conflicting vehicle speeds, are first measured from video observations at signalized intersections. The conflicts are then classified into three severity levels using a risk score function defined by these measures. Multiple linear regression models are developed to relate left turn opposed crashes at the same intersections in the period 2009-2014 to the correspondingly classified conflicts. The results show strong relationships between the classified conflicts and crashes (adjusted",
language="en",
issn="0361-1981",
doi="10.1177/0361198120986167",
url="http://dx.doi.org/10.1177/0361198120986167"
}