TY  - JOUR
PY  - 2021//
TI  - Incorporating speed in a traffic conflict severity index to estimate left turn opposed crashes at signalized intersections
JO  - Transportation research record
A1  - Anarkooli, Alireza Jafari
A1  - Persaud, Bhagwant
A1  - Milligan, Craig
A1  - Penner, Joel
A1  - Saleem, Taha
SP  - 214
EP  - 225
VL  - 2675
IS  - 5
N2  - 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
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/0361198120986167
ID  - ref1
ER  -