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

Hewett, Nicola; Golightly, Andrew; Fawcett, Lee; Thorpe, Neil

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 dependent 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 the US, and find that the model provides a good description of the underlying process and reasonable forecast accuracy.

Citation

Hewett, N., Golightly, A., Fawcett, L., & Thorpe, N. (2024). Bayesian inference for a spatio-temporal model of road traffic collision data. Journal of Computational Science, 80, Article 102326. https://doi.org/10.1016/j.jocs.2024.102326

• Journal Article Type: Article
• Acceptance Date: May 14, 2024
• Online Publication Date: May 30, 2024
• Publication Date: 2024-08
• Deposit Date: Jun 27, 2024
• Publicly Available Date: Jun 27, 2024
• Journal: Journal of Computational Science
• Print ISSN: 1877-7503
• Electronic ISSN: 1877-7511
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 80
• Article Number: 102326
• DOI: https://doi.org/10.1016/j.jocs.2024.102326
• Public URL: https://durham-repository.worktribe.com/output/2504488