Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis.
In the US, single-vehicle run-off-roadway accidents result in a million highway crashes with roadside features every year and account for approximately one third of all highway fatalities. Despite the number and severity of run-off-roadway accidents, quantification of the effect of possible countermeasures has been surprisingly limited due to the absence of data (particularly data on roadside features) needed to rigorously analyze factors affecting the frequency and severity of run-off-roadway accidents. This study provides some initial insight into this important problem by combining a number of databases, including a detailed database on roadside features, to analyze run-off-roadway accidents on a 96.6-km section of highway in Washington State. Using zero-inflated count models and nested logit models, statistical models of accident frequency and severity are estimated and the findings isolate a wide range of factors that significantly influence the frequency and severity of run-off-roadway accidents. The marginal effects of these factors are computed to provide an indication on the effectiveness of potential countermeasures. The findings show significant promise in applying new methodological approaches to run-off-roadway accident analysis.
Lee
Jinsun
J
Department of Civil and Environmental Engineering, University of Washington, Seattle 98195-2700, USA.
Mannering
Fred
F
D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical