Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed considering a set of socioeconomic variables and ignoring the measurement error, which can bias the statistical inference. This paper presents a Bayesian model to analyse car crashes occurrences at the network-lattice level, taking into account measurement error in the spatial covariate. The suggested methodology is exemplified by considering the collisions in the road network of Leeds (UK) during 2011-2019. Traffic volumes are approximated using an extensive set of counts obtained from mobile devices and the estimates are adjusted using a spatial measurement error correction.

翻译：道路伤亡事故是现代社会的严峻挑战。近年来，多位学者提出了复杂精细的方法以协助当局实施新政策。这些模型通常基于一组社会经济变量构建，却忽略了可能影响统计推断的测量误差。本文提出了一种贝叶斯模型，用于分析网络格点层面的车祸事件，同时考虑空间协变量中的测量误差。以英国利兹市2011-2019年道路网络碰撞事件为例，论证该方法。交通流量通过移动设备获取的大量计数近似估算，并采用空间测量误差校正对估计值进行调整。