title: A novel Bayesian hierarchical model for road safety hotspot prediction.
abstract: In this paper, we 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 our 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. Our 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 our uncertainty in these predictions. The main contributions of our 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. We illustrate our model with an application to accident counts at 734 potential hotspots in the German city of Halle; we also propose some simple diagnostics to validate the predictive capability of our model. We conclude that our model accurately predicts future accident counts, with point estimates from the predictive distribution matching observed counts extremely well.
authors.affiliation: School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: lee.fawcett@ncl.ac.uk.
authors.forename: Lee
authors.initials: L
authors.lastname: Fawcett
authors.affiliation: School of Civil Engineering & Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: neil.thorpe@ncl.ac.uk.
authors.forename: Neil
authors.initials: N
authors.lastname: Thorpe
authors.affiliation: School of Mathematics & Statistics, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: j.matthews2@ncl.ac.uk.
authors.forename: Joseph
authors.initials: J
authors.lastname: Matthews
authors.affiliation: PTV GROUP, Traffic Software - Safety Development, Haid-und-Neu-Str. 15, 76131 Karlsruhe, Germany. Electronic address: Karsten.Kremer@ptvgroup.com.
authors.forename: Karsten
authors.initials: K
authors.lastname: Kremer
mesh_terms: D000063:Accidents, Traffic / Q000639:trends*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D004781:Environmental Exposure; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D018570:Risk Assessment; D012449:Safety / Q000706:statistics & numerical data*; D006113:United Kingdom
keywords: Accident prediction models; Bayesian statistics; Hotspot prediction; Predictive distribution; Regression-to-mean; Trend