A novel Bayesian hierarchical model for road safety hotspot prediction

In this paper, the authors propose a Bayesian hierarchical model for predicting accident counts in future years at sites within a pool of potential road safety hotspots. The aim is to inform road safety practitioners of the location of likely future hotspots to enable a proactive, rather than reactive, approach to road safety scheme implementation. A feature of their model is the ability to rank sites according to their potential to exceed, in some future time period, a threshold accident count which may be used as a criterion for scheme implementation. The authors' model specification enables the classical empirical Bayes formulation – commonly used in before-and-after studies, wherein accident counts from a single before period are used to estimate counterfactual counts in the after period – to be extended to incorporate counts from multiple time periods. This allows site-specific variations in historical accident counts (e.g. locally-observed trends) to offset estimates of safety generated by a global accident prediction model (APM), which itself is used to help account for the effects of global trend and regression-to-mean (RTM). The Bayesian posterior predictive distribution is exploited to formulate predictions and to properly quantify the authors' uncertainty in these predictions. The main contributions of their model include (i) the ability to allow accident counts from multiple time-points to inform predictions, with counts in more recent years lending more weight to predictions than counts from time-points further in the past; (ii) where appropriate, the ability to offset global estimates of trend by variations in accident counts observed locally, at a site-specific level; and (iii) the ability to account for unknown/unobserved site-specific factors which may affect accident counts. The authors illustrate their model with an application to accident counts at 734 potential hotspots in the German city of Halle; they also propose some simple diagnostics to validate the predictive capability of their model. They conclude that their model accurately predicts future accident counts, with point estimates from the predictive distribution matching observed counts extremely well.

• Record URL:
  • https://doi.org/10.1016/j.aap.2016.11.021
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0001457516304341
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • Crown Copyright © 2016 Published by Elsevier Ltd.
• Authors:
  • Fawcett, Lee
  • Thorpe, Neil
  • Matthews, Joseph
  • Kremer, Karsten
• Publication Date: 2017-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 262-271
• Serial:
  • Accident Analysis & Prevention
  • Volume: 99, Part A
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Crash rates; Crash risk forecasting; High risk locations; Highway safety
• Uncontrolled Terms: Hierarchical Bayes methods
• Geographic Terms: Halle an der Saale (Germany)
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01626938
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 27 2017 9:27AM