Toronto Metropolitan University
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Incorporating Speed in a Traffic Conflict Severity Index to Estimate Left Turn Opposed Crashes at Signalized Intersections

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journal contribution
posted on 2022-07-25, 20:05 authored by Bhagwant PersaudBhagwant Persaud, Alireza Jafari Anarkooli, Craig Milligan, Joel Penner, Taha Saleem


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 for total and fatal/injury crashes, respectively). The results also reveal that the contribution of conflicts to the risk of crashes varies based on speed dimension of their severity, suggesting that neglecting speed as a factor in conflict severity levels may be at the expense of losing meaningful information. The models can be applied to estimate the change in crashes following a safety treatment by observing, through video analytics, the change in conflicts and speeds and using the crash-conflict-speed model. The methodological approach is viable for quickly evaluating all treatments and, in particular, innovative ones for which knowledge on safety effects is sparse or non-existing.


NSERC ApplID RGPIN-2017-04457)