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

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

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Authors

Nicola Hewett

Lee Fawcett

Neil Thorpe



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
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

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