Federal University Reports Findings in Risk Management (Geographically weighted negative binomial regression applied to zonal level safety…

Federal University Reports Findings in Risk Management (Geographically weighted negative binomial regression applied to zonal level safety performance models)

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 out of Fortaleza, Brazil, by VerticalNews editors, research stated, "Generalized Linear Models (GLM) with negative binomial distribution for errors, have been widely used to estimate safety at the level of transportation planning. The limited ability of this technique to take spatial effects into account can be overcome through the use of local models from spatial regression techniques, such as Geographically Weighted Poisson Regression (GWPR)."

Financial supporters for this research include Brazilian National Council for Scientific and Technological Development (CNPq), Ceara State Foundation for Scientific and Technological Development (FUNCAP).

Our news journalists obtained a quote from the research from Federal University, "Although GWPR is a system that deals with spatial dependency and heterogeneity and has already been used in some road safety studies at the planning level, it fails to account for the possible overdispersion that can be found in the observations on road-traffic crashes. Two approaches were adopted for the Geographically Weighted Negative Binomial Regression (GWNBR) model to allow discrete data to be modeled in a non-stationary form and to take note of the overdispersion of the data: the first examines the constant overdispersion for all the traffic zones and the second includes the variable for each spatial unit. This research conducts a comparative analysis between non-spatial global crash prediction models and spatial local GWPR and GWNBR at the level of traffic zones in Fortaleza/Brazil. A geographic database of 126 traffic zones was compiled from the available data on exposdre, network characteristics, socioeconomic factors and land use."

According to the news editors, the research concluded: "The models were calibrated by using the frequency of injury crashes as a dependent variable and the results showed that GWPR and GWNBR achieved a better performance than GLM for the average residuals and likelihood as well as reducing the spatial autocorrelation of the residuals, and the GWNBR model was more able to capture the spatial heterogeneity of the crash frequency."

For more information on this research see: Geographically weighted negative binomial regression applied to zonal level safety performance models. Accident Analysis and Prevention, 2017;106():254-261. 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 journalists report that additional information may be obtained by contacting M. Gomes, Univ Fed Ceara, Dept. of Transportat Engn, Fortaleza, Ceara, Brazil. Additional authors for this research include F. Cunto and A.R. da Silva.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2017.06.011. 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: Fortaleza, Brazil, South America, Risk Management, Federal University.

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2017, NewsRx LLC