title: Internal validation of near-crashes in naturalistic driving studies: a continuous and multivariate approach.
abstract: Large naturalistic driving studies give extremely detailed insight into how traffic accidents happen and what causes them. However, even in very large studies there are only relatively few crashes. Hence one additionally selects and studies crash surrogates, so called "near-crashes", i.e. situations when a crash almost happened. The selection procedures invariably entail severe risks of causing bias. In this paper we use extreme value statistics to develop two methods to study the extent and form of this bias. The methods are applied to a large naturalistic driving study, the 100-car study. Both methods identified a severe discrepancy between the rear-striking near-crashes and the rear-striking crashes. Perhaps surprisingly, one conclusion is that, for rear-striking and in this study, the crashes have little relevance for increasing traffic safety. We believe substantial efforts should be made to develop statistical methods for using near-crashes and crashes in future large naturalistic driving studies (such as the SHRP2 study).
authors.affiliation: Department of Mathematical Statistics, Chalmers and Gothenburg University, SE-412 96 Gothenburg, Sweden. Electronic address: jenny.jonasson@astrazeneca.com.
authors.forename: Jenny K
authors.initials: JK
authors.lastname: Jonasson
authors.forename: Holger
authors.initials: H
authors.lastname: Rootzén
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D006801:Humans; D015999:Multivariate Analysis; D012107:Research Design; D018570:Risk Assessment; D012449:Safety
keywords: Crash surrogate; Extreme value statistics; Naturalistic driving study; Rear-ending crash; Selection bias; Traffic safety