title: Use of crash surrogates and exceedance statistics to estimate road safety.
abstract: The limited ability of existing safety models to properly reflect crash causality has its source in cross-sectional analysis applied to the estimation of the intrinsically complex safety factors with highly aggregated and frequently poor quality of data. The adequacy of the data may be improved thanks to the unprecedented progress in sensing technologies and the invention of the naturalistic driving method of data collection. Proposed in this paper is a new modeling paradigm that integrates several types of safety models. The primary improvement results from a more adequate representation of the crash occurrence process by incorporating crash precursor events into the modeling framework. A Pareto-based estimating method for the likelihood of a collision occurrence, given a precursor event, is explained and illustrated with the simple example of road departures.
authors.affiliation: Center for Road Safety, School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, United States. tarko@purdue.edu
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Tarko
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D001185:Artificial Intelligence; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001696:Biomechanical Phenomena; D015984:Causality; D003625:Data Collection; D006801:Humans; D016013:Likelihood Functions; D008432:Mathematical Computing; D015233:Models, Statistical; D012107:Research Design; D012309:Risk-Taking; D012449:Safety; D012984:Software; D014741:Video Recording