Quantifying safety benefit of winter road maintenance: accident frequency modeling.
This research presents a modeling approach to investigate the association of the accident frequency during a snow storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. The results have the premise to be applied for evaluating different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index as a surrogate measure of the commonly used friction measure to capture different road surface conditions. Data from various data sources, such as weather, road condition observations, traffic counts and accidents, are integrated and used to test three event-based models including the Negative Binomial model, the generalized NB model and the zero inflated NB model. These models are compared for their capability to explain differences in accident frequencies between individual snow storms. It was found that the generalized NB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the road surface condition index was statistically significant influencing the accident occurrence. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods.
Usman
Taimur
T
Department of Civil & Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada. tusman@engmail.uwaterloo.ca
Fu
Liping
L
Miranda-Moreno
Luis F
LF
D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D003430:Cross-Sectional Studies; D006801:Humans; D015233:Models, Statistical; D009864:Ontario; D017751:Safety Management / Q000706:statistics & numerical data*; D012621:Seasons; D012914:Snow