Introduction: Safety performance functions (SPF) are employed to predict crash counts at the different roadway elements. Several SPFs were developed for the various roadway elements based on different classifications such as functional classification and area type. Since a more detailed classification of roadway elements leads to more accurate crash predictions, multiple states have developed new classification systems to classify roads based on a comprehensive classification. In Florida, the new roadway context classification system incorporates geographic, demographic, and road characteristics information. Method: In this study, SPFs were developed in the framework of the FDOT roadway context classification system at three levels of modeling, context classification (CC-SPFs), area type (AT-SPFs), and statewide (SW-SPF) levels. Crash and traffic data from 2015-2019 were obtained. Road characteristics and road environment information have also been gathered along Florida roads for the SPF development. Results: The developed SPFs showed that there are several variables that influence the frequency of crashes, such as annual average daily traffic (AADT), signalized intersections and access point densities, speed limit, and shoulder width. However, there are other variables that did not have an influence in crash occurrence such as concrete surface and the presence of bicycle slots. CC-SPFs had the best performance among others. Moreover, network screening to determine the most problematic road segments has been accomplished. The results of the network screening indicated that the most problematic roads in Florida are the suburban commercial and the urban general roads. Practical Applications: This research provides a solid reference for decision-makers regarding crash prediction and safety improvement along Florida roads.

简介：采用安全性能函数 (SPF) 来预测不同道路元素的碰撞次数。基于不同的分类，如功能分类和区域类型，为各种道路元素开发了几个 SPF。由于道路要素的更详细分类导致更准确的碰撞预测，因此多个州开发了新的分类系统，以基于综合分类对道路进行分类。在佛罗里达州，新的道路环境分类系统结合了地理、人口和道路特征信息。方法：在本研究中，SPF 是在 FDOT 道路环境分类系统的框架下开发的，分为三个级别的建模：环境分类 (CC-SPFs)、区域类型 (AT-SPFs) 和全州 (SW-SPF) 级别。获得了 2015-2019 年的车祸和交通数据。还收集了佛罗里达州道路沿线的道路特征和道路环境信息，用于 SPF 开发。结果：开发的 SPF 表明有几个变量会影响碰撞频率，例如年平均每日交通量 (AADT)、信号交叉口和接入点密度、速度限制和肩宽。但是，还有其他变量对碰撞的发生没有影响，例如混凝土表面和自行车槽的存在。CC-SPF 的性能最好。此外，已经完成了确定最有问题的路段的网络筛选。网络筛选结果表明，佛罗里达州问题最多的道路是郊区商业道路和城市普通道路。实际应用：这项研究为决策者提供了有关佛罗里达州道路碰撞预测和安全改进的可靠参考。