This study develops a real-time framework for estimating pedestrian crash risk at signalized intersections under heterogeneous, non-lane-based traffic. Existing approaches often assume linear relationships between covariates and parameters, oversimplifying the complex, non-monotonic interactions among different road users. To overcome this, the framework introduces a non-linear link function within a Bayesian generalized extreme value (GEV) structure to capture traffic variability more accurately. The framework applies extreme value theory through the block maxima approach using post-encroachment time as a surrogate safety measure. A hierarchical Bayesian model incorporating both linear and non-linear link functions into GEV parameters is estimated using Markov Chain Monte Carlo simulation. It also introduces a behavior-normalized Modified Crash Risk (MRC) formula to account for pedestrians' habitual risk-taking behavior. Seven Bayesian hierarchical models were developed and compared using deviance information criterion. Models employing non-linear link functions for the location and scale parameters significantly outperformed their linear counterparts. The results revealed that pedestrian speed has a negative relationship with crash risk, while flow and speed of motorized vehicles, pedestrian flow, and non-motorized vehicles conflicting speed contribute positively. The MRC formulation reduced overestimation and provided crash predictions with 93% confidence. The integration of non-linear link functions enhances model flexibility, capturing the non-linear nature of traffic extremes. The proposed MRC metric aligns crash risk estimates with real-world pedestrian behavior in mixed-traffic environments. This framework offers a practical analytical tool for traffic engineers and planners to design adaptive signal control and pedestrian safety interventions before crashes occur.

翻译：本研究开发了一个实时框架，用于评估异质化、非基于车道交通环境下信号控制交叉口的行人碰撞风险。现有方法通常假设协变量与参数之间存在线性关系，过度简化了不同道路使用者之间复杂且非单调的相互作用。为克服此局限，该框架在贝叶斯广义极值（GEV）结构中引入了一种非线性链接函数，以更准确地捕捉交通变异性。该框架通过块最大值方法应用极值理论，并以侵占后时间作为替代安全度量指标。采用马尔可夫链蒙特卡罗模拟估计了一个将线性和非线性链接函数同时纳入GEV参数的分层贝叶斯模型。研究还引入了行为归一化的修正碰撞风险（MRC）公式，以考虑行人习惯性冒险行为。共开发了七个贝叶斯分层模型，并通过偏差信息准则进行比较。对位置参数和尺度参数采用非线性链接函数的模型性能显著优于线性模型。结果表明，行人速度与碰撞风险呈负相关，而机动车流量与速度、行人流量以及非机动车冲突速度则产生正向影响。MRC公式减少了风险高估，并以93%的置信度提供碰撞预测。非线性链接函数的集成增强了模型灵活性，能够捕捉交通极值事件的非线性特征。所提出的MRC度量使碰撞风险估计与混合交通环境中的实际行人行为相契合。该框架为交通工程师和规划者提供了实用的分析工具，可在事故发生前设计自适应信号控制及行人安全干预措施。