By a News Reporter-Staff News Editor at Insurance Weekly News -- Investigators publish new report on Risk Management. According to news reporting from Orlando, Florida, by VerticalNews journalists, research stated, "Severe crashes are causing serious social and economic loss, and because of this, reducing crash injury severity has become one of the key objectives of the high speed facilities' (freeway and expressway) management. Traditional crash injury severity analysis utilized data mainly from crash reports concerning the crash occurrence information, drivers' characteristics and roadway geometric related variables."
The news correspondents obtained a quote from the research from the University of Central Florida, "In this study, real-time traffic and weather data were introduced to analyze the crash injury severity. The space mean speeds captured by the Automatic Vehicle Identification (AVI) system on the two roadways were used as explanatory variables in this study; and data from a mountainous freeway (1-70 in Colorado) and an urban expressway (State Road 408 in Orlando) have been used to identify the analysis result's consistence. Binary probit (BP) models were estimated to classify the non-severe (property damage only) crashes and severe (injury and fatality) crashes. Firstly, Bayesian BP models' results were compared to the results from Maximum Likelihood Estimation BP models and it was concluded that Bayesian inference was superior with more significant variables. Then different levels of hierarchical Bayesian BP models were developed with random effects accounting for the unobserved heterogeneity at segment level and crash individual level, respectively. Modeling results from both studied locations demonstrate that large variations of speed prior to the crash occurrence would increase the likelihood of severe crash occurrence. Moreover, with considering unobserved heterogeneity in the Bayesian BP models, the model goodness-of-fit has improved substantially."
According to the news reporters, the research concluded: "Finally, possible future applications of the model results and the hierarchical Bayesian probit models were discussed."
For more information on this research see: Using hierarchical Bayesian binary probit models to analyze crash injury severity on high speed facilities with real-time traffic data. Accident Analysis and Prevention, 2014;62():161-167. 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 journalists report that additional information may be obtained by contacting R.J. Yu, University of Central Florida, Dept. of Civil Environm & Construct Engn, Orlando, FL 32826, United States.
Keywords for this news article include: Orlando, Florida, United States, Risk Management, North and Central America
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