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Use of crash surrogates and exceedance statistics to estimate road safety.
MedLine Citation:
PMID:  22269505     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
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:
Andrew P Tarko
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Publication Detail:
Type:  Journal Article     Date:  2011-09-08
Journal Detail:
Title:  Accident; analysis and prevention     Volume:  45     ISSN:  1879-2057     ISO Abbreviation:  Accid Anal Prev     Publication Date:  2012 Mar 
Date Detail:
Created Date:  2012-01-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  1254476     Medline TA:  Accid Anal Prev     Country:  England    
Other Details:
Languages:  eng     Pagination:  230-40     Citation Subset:  IM    
Copyright Information:
Copyright © 2011 Elsevier Ltd. All rights reserved.
Affiliation:
Center for Road Safety, School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, United States.
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