Predicting cycling accident risk in Brussels: A spatial case–control approach

• Published in: Accident analysis and prevention 2014-01, Vol.62, p.341-357
• Main Authors: Vandenbulcke, Grégory, Thomas, Isabelle, Int Panis, Luc
• Format: Article
• Language: English
• Subjects: Accident risk; Accident scenes; Accidents; Accidents, Traffic - statistics & numerical data; Bayes Theorem; Bayesian framework; Belgium; Bicycle accident; Bicycles; Bicycling - injuries; Bicycling - statistics & numerical data; Biological and medical sciences; Case-Control Studies; Case–control strategy; City Planning - statistics & numerical data; Cycles; Cycling; Environment Design - statistics & numerical data; Humans; Logistic Models; Mathematical models; Medical sciences; Miscellaneous; Prevention and actions; Public health. Hygiene; Public health. Hygiene-occupational medicine; Risk; Risk Assessment; Risk Factors; Spatial Analysis; Traffic engineering; Traffic flow
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2013.07.001

•Cycling accident risks are calculated using an innovative case–control framework.•On-road tram tracks, bridges and complex intersections carry the highest risk.•Shopping centres, van and truck traffic and parked vehicles also increase risk.•Contraflow cycling reduces the accident risk.•Accident risks may be high in points where no cycling accident is yet reported. 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.