title: Predicting cycling accident risk in Brussels: a spatial case-control approach.
abstract: This paper aims at predicting cycling accident risk for an entire network and identifying how road infrastructure influences cycling safety in the Brussels-Capital Region (Belgium). A spatial Bayesian modelling approach is proposed using a binary dependent variable (accident, no accident at location i) constructed from a case-control strategy. Control sites are sampled along the 'bikeable' road network in function of the potential bicycle traffic transiting in each ward. Risk factors are limited to infrastructure, traffic and environmental characteristics. Results suggest that a high risk is statistically associated with the presence of on-road tram tracks, bridges without cycling facility, complex intersections, proximity to shopping centres or garages, and busy van and truck traffic. Cycle facilities built at intersections and parked vehicles located next to separated cycle facilities are also associated with an increased risk, whereas contraflow cycling is associated with a reduced risk. The cycling accident risk is far from being negligible in points where there is actually no reported cycling accident but where they are yet expected to occur. Hence, mapping predicted accident risks provides planners and policy makers with a useful tool for accurately locating places with a high potential risk even before accidents actually happen. This also provides comprehensible information for orienting cyclists to the safest routes in Brussels.
authors: Center for Operations Research and Econometrics (CORE), Université catholique de Louvain (UCL), 34 Voie du Roman Pays, Louvain-la-Neuve B-1348, Belgium.
authors: Grégory
authors: G
authors: Vandenbulcke
authors: Isabelle
authors: I
authors: Thomas
authors: Luc
authors: L
authors: Int Panis
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D001530:Belgium; D001642:Bicycling / Q000293:injuries / Q000706:statistics & numerical data*; D016022:Case-Control Studies; D002958:City Planning / Q000706:statistics & numerical data*; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D018570:Risk Assessment; D012307:Risk Factors; D062206:Spatial Analysis
keywords: Accident risk; Bayesian framework; Bicycle accident; Case–control strategy; Cycling; Risk factors