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:改善道路安全非常重要,因为世界公路上死亡人数仍然高得令人无法接受;每年估计有135万人死亡(世卫组织,2020年)。目前处理碰撞热点的做法几乎总是被动反应的:一旦在某些预先确定的观察期间超过碰撞的临界水平,就采用处理方法(例如道路安全摄像机)。然而,最近,已经制定了方法,预测未来时间段潜在热点的碰撞计数,以期更积极主动地处理道路安全热点。动态线性模型为预测碰撞提供了一个灵活的框架,从而促成这种积极处理。在本文件中,我们展示了如何利用这些模型来捕捉季节性变化和若干地点在时间周期碰撞率方面的空间依赖性。模型允许对完全观测到的地点和缺少某些数据的地点进行抽样内外预测。我们用来自美国北佛罗里达州8个交通管理局区的碰撞率数据来说明我们的方法,我们发现该模型很好地描述了基本过程和合理的预测准确性。