Introduction: Pedestrian safety is a major concern as traffic crashes are the leading cause of fatalities and injuries for commuters. Traffic safety research in the past has developed various strategies to counteract traffic crashes, including the safety performance function (SPF). However, there is still a need for research dedicated to enhancing the SPF for pedestrians from perspectives of methodological framework and data input. To fill this gap, this study aims to add to the current SPF development practice literature by focusing on pedestrian-involved collisions, while considering the typical vehicle ones as well. Methods: First, bivariate models are used to account for the common unobserved heterogeneity shared by the pedestrian- and vehicle-related crashes at the same intersections. Second, variable importance ranking technique is used, along with correlation analysis, to determine mode-specific feature input. Third, the exposure information for both modes, annual pedestrian count, and annual daily vehicles traveled are used for model development. Fourth, a recent Bayesian inference approach (integrated nested Laplace approximation (INLA)) was adopted for bivariate setting. Finally, different evaluation criteria are used to facilitate comprehensive model assessment. Results: The results reveal different statistically significant factors contributing to each of the modes. The offset intersection provides better safety performance for both pedestrians and drivers as compared to other intersection designs. The model findings also corroborate the sensibility of using the bivariate models, rather than the separate univariate ones. Practical Applications: The study shows that pedestrians are more vulnerable to various intersection features such as left-turn channelization, intersection control, urban and rural population group, presence of signal mastarm on the cross-street, and mainline average daily traffic. Greater focus should be directed toward such intersection features to improve pedestrian safety.

简介：行人安全是一个主要问题，因为交通事故是导致通勤者伤亡的主要原因。过去的交通安全研究开发了各种策略来应对交通事故，包括安全性能函数 (SPF)。然而，仍然需要研究致力于从方法框架和数据输入的角度提高行人的 SPF。为了填补这一空白，本研究旨在通过关注与行人相关的碰撞，同时考虑典型的车辆碰撞，来增加当前的 SPF 开发实践文献。方法：首先，使用双变量模型来解释相同交叉路口的行人和车辆相关碰撞所共享的常见的未观察到的异质性。其次，使用变量重要性排序技术以及相关性分析来确定特定于模式的特征输入。第三，两种模式的暴露信息、每年的行人数量和每年每天行驶的车辆都用于模型开发。第四，最近的贝叶斯推理方法（集成嵌套拉普拉斯近似（INLA））被用于双变量设置。最后，使用不同的评估标准来促进综合模型评估。结果：结果揭示了对每种模式有贡献的不同统计显着因素。与其他交叉路口设计相比，偏置交叉路口为行人和司机提供了更好的安全性能。模型结果也证实了使用双变量模型而不是单独的单变量模型的敏感性。实际应用：研究表明，行人更容易受到各种十字路口特征的影响，例如左转通道化、十字路口控制、城乡人口群体、十字路口信号灯的存在以及主线平均每日交通量。应更加关注此类交叉路口的特征，以提高行人安全。