Monitoring day and dark traffic collisions in Toronto neighbourhoods with implications for injury reduction and Vision Zero initiatives: A spatial analysis approach
运用贝叶斯空间模型分析多伦多社区白天与夜间交通碰撞致死重伤风险，发现夜间风险较高的区域，为“零死亡愿景”政策提供重点区域参考。
• Vision Zero policy improves road safety and reduces traffic accidents and injuries. • Shared-component spatial modeling identifies area-specific risks in day and dark. • Space & time analysis identifies area-specific and mean area trends of injuries. • Bayesian probability estimation and GIS show spatiotemporal hotspots & cold spots. • Spatial epidemiology of socioeconomic, deprivation, & marginalization risk factors. The City of Toronto adopted a Vision Zero strategy in 2016 that aims to eliminate deaths and serious injuries from vehicular collisions. The strategy includes policies to improve lighting to reduce collision risks, and past research has suggested lighting as a road safety factor. We apply Bayesian spatial analysis (including Poisson log-normal regression modelling, shared component spatial modelling, and Bayesian spatiotemporal modelling) to publicly available data on traffic collisions where persons are killed or seriously injured (KSI) based on Day/Dark conditions. We assess (1) links between KSI risk and socioeconomic and built environment factors, (2) spatial distributions of relative Day & Dark KSI risk, and (3) area-specific trends in space and time for Day-Dark KSI risk change across Toronto neighbourhoods. Our analysis does not find significant associations between socioeconomic/built environment factors and KSI risk, but we uncover neighbourhoods with heightened Dark KSI risk and pronounced Day-Dark KSI changes compared to Toronto’s mean area trend. Findings highlight the need for increased policy attention for impacts of lighting on collisions and provide insight for focus regions for improved Vision Zero policy development.