Findings on Risk Management Reported by Investigators at University of Texas (Do safety performance functions used for predicting crash frequency…

Findings on Risk Management Reported by Investigators at University of Texas (Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial ...)

By a News Reporter-Staff News Editor at Insurance Weekly News -- New research on Risk Management is the subject of a report. According to news originating from Austin, Texas, by VerticalNews correspondents, research stated, "Safety Performance Functions (SPFs) provide a basis for identifying locations where countermeasures can be effective. While SPFs in the Highway Safety Manual (HSM) were calibrated based on data from select states, calibration factors can be developed to localize SPFs to other states."

Funders for this research include Southeastern Transportation Center, Center for Transportation Research, and the Transportation Engineering and Science Program at University of Tennessee, Office of Secretary of Transportation.

Our news journalists obtained a quote from the research from the University of Texas, "Calibration factors typically provide a coarse adjustment time and space stationarity of associations between crash frequencies and various factors is still assumed, implying that the SPF functional form is transferable. However, with increasing availability of state-wide geo-referenced safety data, new spatial analysis methods, and increasing computational power, it is possible to relax the stationarity assumption. Specifically, to address spatial heterogeneity in SPFs, this study proposes relaxing SPFs (referring to them as Localized SPFs (L-SPFs)) that can be developed by using sophisticated geo-spatial modeling techniques that allow correlates of crash frequencies to vary in space. For demonstration, a 2013 geo-referenced freeway crash and traffic database from Virginia is used. As a potential methodological alternative, crash frequencies are predicted by estimating Geographically Weighted Negative Binomial Regressions. This model significantly outperforms the traditional negative binomial model in terms of model goodness-of-fit, providing a better and fuller understanding of spatial variations in modeled relationships. Our study results uncover significant spatial variations in parameter estimates for Annual Average Daily Traffic (AADT) and segment length. Ignoring such variations can result in prediction errors. The results indicate low transferability of a single statewide SPF highlighting the importance of developing L-SPFs."

According to the news editors, the research concluded: "From a practical standpoint, L-SPFs can better predict crash frequencies and support prioritizing safety improvements in specific locations."

For more information on this research see: Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity. Accident Analysis and Prevention, 2017;109():132-142. 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/)

The news correspondents report that additional information may be obtained from J. Liu, Univ Texas Austin, CTR, Austin, TX 78712, United States. Additional authors for this research include A.J. Khattak and B. Wali.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2017.10.012. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Keywords for this news article include: Austin, Texas, United States, North and Central America, Risk Management, University of Texas.

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