title: Crashes and crash-surrogate events: exploratory modeling with naturalistic driving data.
abstract: There is a need to extend and refine the use of crash surrogates to enhance safety analyses. This is particularly true given opportunities for data collection presented by naturalistic driving studies. This paper connects the original research on traffic conflicts to the contemporary literature concerning crash surrogates using the crash-to-surrogate ratio, π. A conceptual structure is developed in which the ratio can be estimated using either a Logit or Probit formulation which captures context and event variables as predictors in the model specification. This allows the expansion of the crash-to-surrogate concept beyond traffic conflicts to many contexts and crash types. The structure is tested using naturalistic driving data from a study conducted in the United States (Dingus et al., 2005). While the sample size is limited (13 crashes and 38 near crashes), there is reasonable correspondence between predicted and observed crash frequencies using a Logit model formulation. The paper concludes with a summary of empirical results and suggestions for future research.
authors.affiliation: Civil and Environmental Engineering, Larson Institute, Penn State, University Park, PA 16802, USA. kxw930@psu.edu
authors.forename: Kun-Feng
authors.initials: KF
authors.lastname: Wu
authors.forename: Paul P
authors.initials: PP
authors.lastname: Jovanis
mesh_terms: D000054:Acceleration; D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D001642:Bicycling / Q000293:injuries; D016001:Confidence Intervals; D003220:Conflict, Psychological; D003625:Data Collection; D004779:Environment Design; D006801:Humans; D016015:Logistic Models; D011336:Probability; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012449:Safety / Q000592:standards*; D014481:United States; D016138:Walking / Q000293:injuries