Chaudhuri Somnath, Juan Pablo, Mateu Jorge
Research Group on Statistics, Econometrics and Health (GRECS), University of Girona, Girona, Spain.
IMAC, University Jaume I, Castellón, Spain.
J Appl Stat. 2022 Jul 29;50(16):3229-3250. doi: 10.1080/02664763.2022.2104822. eCollection 2023.
Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network. A Bayesian methodology using Integrated nested Laplace approximations with stochastic partial differential equations (SPDE) has been applied in the modeling process. As a novelty, we have introduced SPDE network triangulation to estimate the spatial autocorrelation restricted to the linear network. The resulting risk maps provide information to identify safe routes between source and destination points, and can be useful for accident prevention and multi-disciplinary road safety measures.
交通死亡和伤害是全球主要的公共卫生问题之一。本研究考虑城市环境中的事故记录，以探索和分析道路交通事故发生率的时空特征。我们提出了一个时空模型，用于预测任何给定路段上的交通碰撞次数，进而生成整个道路网络的风险地图。在建模过程中应用了一种贝叶斯方法，该方法使用带有随机偏微分方程（SPDE）的集成嵌套拉普拉斯近似。作为一项创新，我们引入了SPDE网络三角测量法来估计限于线性网络的空间自相关性。由此产生的风险地图为识别源点和目的地之间的安全路线提供了信息，并且可用于事故预防和多学科道路安全措施。
