Department of Civil & Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Accid Anal Prev. 2010 Nov;42(6):1878-87. doi: 10.1016/j.aap.2010.05.008. Epub 2010 Jun 8.
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.
本研究提出了一种建模方法，以调查在暴风雪事件期间事故频率与路面状况、能见度和其他控制交通暴露的因素之间的关联。这些结果有前提条件可以应用于评估不同的维护策略，以安全为绩效指标。作为该方法的一部分，本研究引入了路面状况指数作为常用摩擦测量的替代指标，以捕捉不同的路面状况。来自各种数据源（如天气、路况观测、交通计数和事故）的数据被整合起来，并用于测试三种基于事件的模型，包括负二项式模型、广义 NB 模型和零膨胀 NB 模型。这些模型被比较，以确定它们在解释单个暴风雪之间事故频率差异方面的能力。研究发现，广义 NB 模型最适合数据，并且最能够捕捉除过剩零之外的异质性。在主要结果中，发现路面状况指数对事故发生具有统计学意义的影响。本研究首次在非聚合（基于事件）水平上展示了安全与路面状况之间的经验关系，从而可以量化替代维护目标和方法的安全效益。
