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

Lookup NU author(s): Dr Nicola Hewett, Dr Andrew Golightly, Dr Lee Fawcett, Dr Neil Thorpe

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2024 The Author(s). 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.


Publication metadata

Author(s): Hewett N, Golightly A, Fawcett L, Thorpe N

Publication type: Article

Publication status: Published

Journal: Journal of Computational Science

Year: 2024

Volume: 80

Print publication date: 01/08/2024

Online publication date: 17/05/2024

Acceptance date: 14/05/2024

Date deposited: 11/06/2024

ISSN (print): 1877-7503

ISSN (electronic): 1877-7511

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.jocs.2024.102326

DOI: 10.1016/j.jocs.2024.102326

Data Access Statement: Data will be made available on request.


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