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Lookup NU author(s): Dr Nicola Hewett, Dr Andrew Golightly, Dr Lee Fawcett, Dr Neil Thorpe
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 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.
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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