Abstract

Safety performance functions (SPFs) are vital tools used to predict and reduce intersection crashes. Because SPFs developed in the Highway Safety Manual (HSM) only use data from certain states, several states have developed region-specific SPFs. However, these SPFs typically only utilize the three roadway categories in the HSM. This research developed SPFs based on a new context classification system used by the Florida Department of Transportation (FDOT) which categorizes intersections into eight different categories. Zero-inflated negative binomial (ZINB), zero-inflated Poisson, and hurdle models were developed and compared to the commonly used negative binomial (NB) and Poisson models for four context classification groups. To develop these context-specific SPFs, data for 29 variables were collected based on the Model Inventory of Roadway Elements 2.0, allowing for standard data collection across agencies. A statistically significant linear regression model (adjusted R² = 0.684) was built to predict missing minor AADT volumes. ZINB models outperformed the other models for the two unsignalized intersection groups, whereas NB models performed the best for the two signalized intersection groups. The influential variables differed for each group, showing how FDOT’s context classification system can identify specific crash-influencing factors for different classifications, helping agencies better reduce intersection crashes.

摘要

安全性能函数 (SPF) 是用于预测和减少交叉路口碰撞的重要工具。由于公路安全手册 (HSM) 中制定的 SPF 仅使用来自某些州的数据，因此有几个州制定了针对特定地区的 SPF。但是，这些 SPF 通常仅使用 HSM 中的三个道路类别。这项研究基于佛罗里达州交通部 (FDOT) 使用的一种新的上下文分类系统开发了 SPF，该系统将交叉口分为八种不同的类别。开发了零膨胀负二项式 (ZINB)、零膨胀泊松和障碍模型，并与四个上下文分类组的常用负二项式 (NB) 和泊松模型进行了比较。为了开发这些特定于上下文的 SPF，基于道路要素模型清单 2.0 收集了 29 个变量的数据，允许跨机构收集标准数据。统计显着的线性回归模型（调整R ² = 0.684) 用于预测丢失的次要 AADT 卷。ZINB 模型在两个无信号交叉口组中的表现优于其他模型，而 NB 模型在两个信号交叉口组中表现最好。每个组的影响变量不同，显示了 FDOT 的上下文分类系统如何识别不同分类的特定碰撞影响因素，帮助机构更好地减少交叉口碰撞。