Findings from Seoul National University Yields New Findings on Risk Management (Finite mixture modeling for vehicle crash data with application to hotspot identification)
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 reporting originating in Seoul, South Korea, by VerticalNews journalists, research stated, "The application of finite mixture regression models has recently gained an interest from highway safety researchers because of its considerable potential for addressing unobserved heterogeneity. Finite mixture models assume that the observations of a sample arise from two or more unobserved components with unknown proportions."
The news reporters obtained a quote from the research from Seoul National University, "Both fixed and varying weight parameter models have been shown to be useful for explaining the heterogeneity and the nature of the dispersion in crash data. Given the superior performance of the finite mixture model, this study, using observed and simulated data, investigated the relative performance of the finite mixture model and the traditional negative binomial (NB) model in terms of hotspot identification. For the observed data, rural multilane segment crash data for divided highways in California and Texas were used. The results showed that the difference measured by the percentage deviation in ranking orders was relatively small for this dataset. Nevertheless, the ranking results from the finite mixture model were considered more reliable than the NB model because of the better model specification. This finding was also supported by the simulation study which produced a high number of false positives and negatives when a mis-specified model was used for hotspot identification. Regarding an optimal threshold value for identifying hotspots, another simulation analysis indicated that there is a discrepancy between false discovery (increasing) and false negative rates (decreasing)."
According to the news reporters, the research concluded: "Since the costs associated with false positives and false negatives are different, it is suggested that the selected optimal threshold value should be decided by considering the trade-offs between these two costs so that unnecessary expenses are minimized."
For more information on this research see: Finite mixture modeling for vehicle crash data with application to hotspot identification. Accident Analysis and Prevention, 2014;71():319-326. 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.elsevier.com/wps/product/cws_home/336)
Our news correspondents report that additional information may be obtained by contacting B.J. Park, Seoul National University, Dept. of Civil & Environm Engn, Seoul 151, South Korea. Additional authors for this research include D. Lord and C. Lee.
Keywords for this news article include: Seoul, South Korea, Asia, Risk Management
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