Crash prediction models (CPM) are mostly used for network screening in the road safety management process. The Highway Safety Manual (HSM) offers consistent and reliable CPMs for various roadway facilities that are commonly known as safety performance functions (SPFs). SPFs are statistical regression models that estimate the expected crash frequencies by crash severity, type, and facility types as a function of highway geometric characteristics and traffic exposure. They are vital in identifying high-frequency crash locations and assessing the effectiveness of safety countermeasures. HSM SPFs were originally developed using data collected from a selected few states in the USA. When applied to different jurisdictions, agencies can either develop local SPFs or calibrate the existing HSM base SPFs for local conditions depending on various trade-offs. This study aims to calibrate HSM-default SPFs for multilanes rural divided highway segments using three years of crash data (2017–2019) in the Kingdom of Saudi Arabia (KSA). In this regard, two highways (NHWY-80 and NHWY-85) in the eastern region were considered for the analysis. Crash and traffic data were procured from the MOT (Ministry of Transport), Riyadh, KSA. Geometric data was collected from MOT as well as google earth and field surveys. Calibration procedure as recommended by HSM was followed to obtain the local calibration factors. The Interactive Highway Safety Design Model (IHSDM) calibrator tool was used for the analysis. SPFs calibration results revealed that HSM predictive methodology consistently overpredicts all types of crashes (i.e., total, fatal and injury, and property damage crashes) on both highways. The estimated calibration factors ranged from 0.53 to 0.78. Various goodness of fit (GOF) measures (like MAD, MSPE, MPB) were used for quality assessment of calibrated SPFs. Methods used in this study could be beneficially practiced in any jurisdiction. Calibrated SPFs provide a favorable alternative and replacement of HSM-default SPFs, thereby making the crash predictions more accurate and thus helps in better decision-making related to highway safety.

碰撞预测模型（CPM）主要用于道路安全管理过程中的网络筛选。公路安全手册 (HSM) 为各种道路设施提供一致且可靠的 CPM，通常称为安全性能功能 (SPF)。SPF 是统计回归模型，它根据碰撞严重程度、类型和设施类型估计预期碰撞频率，作为高速公路几何特征和交通暴露的函数。它们对于识别高频碰撞位置和评估安全对策的有效性至关重要。HSM SPF 最初是使用从美国选定的几个州收集的数据开发的。当应用于不同的司法管辖区时，机构可以根据各种权衡来制定本地 SPF 或根据当地条件校准现有的 HSM 基础 SPF。本研究旨在使用沙特阿拉伯王国 (KSA) 三年的碰撞数据（2017-2019 年）校准多车道农村分隔公路路段的 HSM 默认 SPF。对此，考虑了东部地区的两条高速公路（NHWY-80和NHWY-85）进行分析。碰撞和交通数据来自沙特阿拉伯利雅得的 MOT（交通部）。几何数据来自 MOT 以及谷歌地球和实地调查。遵循 HSM 推荐的校准程序以获得本地校准系数。交互式公路安全设计模型 (IHSDM) 校准器工具用于分析。SPF 校准结果表明，HSM 预测方法始终高估了两条高速公路上的所有类型的碰撞（即，总体、致命和伤害以及财产损失碰撞）。估计的校准因子范围从 0.53 到 0.78。各种拟合优度 (GOF) 测量值（如 MAD、MSPE、MPB）用于校准 SPF 的质量评估。本研究中使用的方法可以在任何司法管辖区进行有益的实践。校准的 SPF 提供了一个有利的替代方案和替代 HSM 默认的 SPF，从而使碰撞预测更加准确，从而有助于更好地做出与高速公路安全相关的决策。