Improving road safety is hugely important with the number of deaths on the world's roads remaining unacceptably high; an estimated 1.35 million people die each year (WHO, 2020). Current practice for treating collision hotspots is almost always reactive: once a threshold level of collisions has been exceeded during some predetermined observation period, treatment is applied (e.g. road safety cameras). However, more recently, methodology has been developed to predict collision counts at potential hotspots in future time periods, with a view to a more proactive treatment of road safety hotspots. Dynamic linear models provide a flexible framework for predicting collisions and thus enabling such a proactive treatment. In this paper, we demonstrate how such models can be used to capture both seasonal variability and spatial dependence in time course collision rates at several locations. The model allows for within- and out-of-sample forecasting for locations which are fully observed and for locations where some data are missing. We illustrate our approach using collision rate data from 8 Traffic Administration Zones in North Florida, USA, and find that the model provides a good description of the underlying process and reasonable forecast accuracy.

翻译：改善道路安全至关重要，全球道路死亡人数仍居高不下，每年约有135万人丧生（WHO，2020）。当前处理碰撞热点区域的方法几乎都是反应性的：一旦在预设观察期内碰撞数量超过阈值，便会采取干预措施（如安装道路安全摄像头）。然而，近年来已发展出预测未来时段潜在热点区域碰撞次数的方法，旨在实现更主动的道路安全热点治理。动态线性模型为预测碰撞并提供此类主动治理方案提供了灵活框架。本文展示了如何利用此类模型同时捕捉多个地点碰撞率时间序列中的季节变异性和空间依赖性。该模型适用于完全观测地点及存在数据缺失地点的样本内预测与样本外预测。我们采用美国佛罗里达州北部8个交通管理区的碰撞率数据验证该方法，结果表明该模型能有效描述基础过程并具备合理的预测精度。