Addressing spatial and temporal heterogeneity in pedestrian crash severity: a partially constrained spatiotemporal logistic regression approach

作者：Qianfang Wang, Pengpeng Xu, Keke Zhang, Qiang Zeng · 发表于：Transportmetrica A Transport Science · 年份：2025 · DOI：10.1080/23249935.2025.2511816 · 被引用次数：4 · 研究领域：Traffic and Road Safety、Urban Transport and Accessibility、Agriculture and Farm Safety

This study investigates the relationship between crash severity and risk factors, addressing the limitations of conventional methods in capturing spatial and temporal heterogeneity. We developed and compared five binary logit models: a basic model, temporal heterogeneity model, spatial heterogeneity model, spatiotemporal heterogeneity, and a partially constrained spatiotemporal heterogeneity model. The latter leverages the posterior distribution properties to ensure similar adjacent time and space regions share distribution. These models were calibrated and validated using 3,222 pedestrian crashes occurring at mid-blocks in Hong Kong (2010-2019). The results showed that both the unconstrained and partially constrained spatiotemporal models outperformed others in fit and predictive accuracy. Temporal heterogeneity analysis revealed evolving risk factors, while spatial heterogeneity analysis identified hot zones for crash severity. Key risk factors included pedestrian age, head injury, pedestrian actions, driver operations, vehicle type, and the first collision point. These insights support targeted, region- and time-specific pedestrian strategies.