New Findings on Risk Management from Texas A&M University Summarized [A Monte-Carlo simulation analysis for evaluating the severity distribution…

New Findings on Risk Management from Texas A&M University Summarized [A Monte-Carlo simulation analysis for evaluating the severity distribution functions (SDFs) calibration methodology and determining the minimum sample-size requirements]

By a News Reporter-Staff News Editor at Insurance Weekly News -- Investigators publish new report on Risk Management. According to news reporting originating in College Station, Texas, by VerticalNews journalists, research stated, "Severity distribution functions (SDFs) are used in highway safety to estimate the severity of crashes and conduct different types of safety evaluations and analyses. Developing a new SDF is a difficult task and demands significant time and resources."

Financial support for this research came from ATLAS Center.

The news reporters obtained a quote from the research from Texas A&M University, "To simplify the process, the Highway Safety Manual (HSM) has started to document SDF models for different types of facilities. As such, SDF models have recently been introduced for freeway and ramps in HSM addendum. However, since these functions or models are fitted and validated using data from a few selected number of states, they are required to be calibrated to the local conditions when applied to a new jurisdiction. The HSM provides a methodology to calibrate the models through a scalar calibration factor. However, the proposed methodology to calibrate SDFs was never validated through research. Furthermore, there are no concrete guidelines to select a reliable sample size. Using extensive simulation, this paper documents an analysis that examined the bias between the 'true' and 'estimated' calibration factors. It was indicated that as the value of the true calibration factor deviates further away from '1', more bias is observed between the 'true' and 'estimated' calibration factors. In addition, simulation studies were performed to determine the calibration sample size for various conditions. It was found that, as the average of the coefficient of variation (CV) of the 'KAB' and 'C' crashes increases, the analyst needs to collect a larger sample size to calibrate SDF models."

According to the news reporters, the research concluded: "Taking this observation into account, sample-size guidelines are proposed based on the average CV of crash severities that are used for the calibration process."

For more information on this research see: A Monte-Carlo simulation analysis for evaluating the severity distribution functions (SDFs) calibration methodology and determining the minimum sample-size requirements. Accident Analysis and Prevention, 2017;98():303-311. Accident Analysis and Prevention can be contacted at: Pergamon-Elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis and Prevention - www.journals.elsevier.com/accident-analysis-and-prevention/)

Our news correspondents report that additional information may be obtained by contacting M. Shirazi, Texas A&M University, Zachry Dept. of Civil Engn, College Stn, TX 77843, United States. Additional authors for this research include S.R. Geedipally and D. Lord.

Keywords for this news article include: College Station, Texas, United States, North and Central America, Risk Management, Texas A&M University.

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