Citation

Anarkooli AJ, Persaud B, Milligan C, Penner J, Saleem T. Transp. Res. Rec. 2021; 2675(5): 214-225.

Copyright

(Copyright © 2021, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198120986167

PMID

unavailable

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