Bayesian inference for a spatio-temporal model of road traffic collision data

TL;DR

This paper presents a Bayesian spatio-temporal model for road traffic collision data that captures seasonal and spatial patterns, enabling proactive hotspot management and accurate forecasting even with missing data.

Contribution

It introduces a Bayesian framework using dynamic linear models to predict collision rates with spatial and seasonal considerations, improving proactive safety measures.

Findings

Model accurately captures seasonal and spatial variability.

Provides reliable forecasts with missing data handling.

Demonstrated effectiveness on North Florida collision data.

Abstract

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.