Qin Xiao, Ivan John N, Ravishanker Nalini
Maricopa Association of Governments, 302 North 1st Avenue Ste. 300, Phoenix, AZ 85003, USA.
Accid Anal Prev. 2004 Mar;36(2):183-91. doi: 10.1016/s0001-4575(02)00148-3.
A critical part of any risk assessment is identifying how to represent exposure to the risk involved. Recent research shows that the relationship between crash count and traffic volume is non-linear; consequently, a simple crash rate computed as the ratio of crash count to volume is not proper for comparing the safety of sites with different traffic volumes. To solve this problem, we describe a new approach for relating traffic volume and crash incidence. Specifically, we disaggregate crashes into four types: (1) single-vehicle, (2) multi-vehicle same direction, (3) multi-vehicle opposite direction, and (4) multi-vehicle intersecting, and define candidate exposure measures for each that we hypothesize will be linear with respect to each crash type. This paper describes initial investigation using crash and physical characteristics data for highway segments in Michigan from the Highway Safety Information System (HSIS). We use zero-inflated-Poisson (ZIP) modeling to estimate models for predicting counts for each of the above crash types as a function of the daily volume, segment length, speed limit and roadway width. We found that the relationship between crashes and the daily volume (AADT) is non-linear and varies by crash type, and is significantly different from the relationship between crashes and segment length for all crash types. Our research will provide information to improve accuracy of crash predictions and, thus, facilitate more meaningful comparison of the safety record of seemingly similar highway locations.
任何风险评估的关键部分都是确定如何表示对所涉及风险的暴露程度。最近的研究表明，事故数量与交通流量之间的关系是非线性的；因此，简单地将事故发生率计算为事故数量与交通流量的比率，并不适合用于比较不同交通流量地点的安全性。为了解决这个问题，我们描述了一种将交通流量与事故发生率联系起来的新方法。具体来说，我们将事故分为四类：（1）单车事故，（2）多车同向事故，（3）多车相向事故，以及（4）多车交叉事故，并为每类事故定义了候选暴露度量，我们假设这些度量与每种事故类型呈线性关系。本文描述了使用来自公路安全信息系统（HSIS）的密歇根州公路路段的事故和物理特征数据进行的初步调查。我们使用零膨胀泊松（ZIP）模型来估计预测上述每种事故类型数量的模型，这些模型是日交通流量、路段长度、限速和道路宽度的函数。我们发现事故与日交通流量（年平均日交通量）之间的关系是非线性的，并且因事故类型而异，而且对于所有事故类型来说，事故与路段长度之间的关系也显著不同。我们的研究将提供信息以提高事故预测的准确性，从而有助于更有意义地比较看似相似的公路位置的安全记录。
