Abstract

Network screening for crash hotspots is the first step in roadway safety management. The empirical Bayes (EB) method has been widely used for ranking sites. In the EB process, the most frequently used model for developing safety performance functions (SPFs) is the negative binomial (NB) regression model, in which the dispersion parameter plays a critical role. There are primarily two forms of the dispersion parameter: fixed and varying. Previous studies showed that SPFs with varying dispersion parameters had better performance in modeling crash estimations, and the Highway Safety Manual has adopted the varying form for segment SPFs. However, a comparative analysis of hotspot identification with fixed and varying dispersion parameters for intersections has not yet been well studied. Moreover, this paper includes a thorough exploration of the varying dispersion parameters with five different functional forms at 1,943 unsignalized intersections in Texas. To evaluate the intersection SPFs, the authors implement three hotspot identification tests in addition to model statistical fit. Two EB approaches with proposed varying dispersion parameters were found superior to the EB approach with a fixed dispersion parameter in hotspot identification. Safety analysts and practitioners are encouraged to consider varying forms of dispersion parameter when analyzing intersection crashes.

摘要

碰撞热点的网络筛查是道路安全管理的第一步。经验贝叶斯 (EB) 方法已被广泛用于网站排名。在 EB 过程中，开发安全性能函数 (SPF) 最常用的模型是负二项式 (NB) 回归模型，其中分散参数起着关键作用。色散参数主要有两种形式：固定的和变化的。先前的研究表明，具有不同分散参数的 SPF 在碰撞估计建模和公路安全手册中具有更好的性能已对分段 SPF 采用了不同的形式。然而，对于交叉口的固定和变化分散参数的热点识别的比较分析尚未得到很好的研究。此外，本文还对德克萨斯州 1,943 个无信号交叉口的五种不同函数形式的不同色散参数进行了深入探索。为了评估交叉点 SPF，除了模型统计拟合外，作者还实施了三个热点识别测试。在热点识别中，发现两种具有不同色散参数的 EB 方法优于具有固定色散参数的 EB 方法。鼓励安全分析师和从业人员在分析交叉口碰撞时考虑不同形式的分散参数。