Accounting for dispersion and correlation in estimating Safety Performance Functions. An overview starting from a case study

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Abstract

In statistical analysis of crash count data, as well as in estimating Safety Performance Functions (SPFs), thefailure of Poisson equidispersion hypothesis and the temporal correlation in annual crash counts must beconsidered to improve the reliability of estimation of the parameters. After a short discussion on the statisticaltools accounting for dispersion and correlation, the paper presents the methodological path followed inestimating a SPF for urban four-leg, signalized intersections. Since the case study exhibited signs ofunderdispersion, a Conway-Maxwell-Poisson Generalized Linear Model (GLM) was fitted to the data; then aquasi-Poisson model in the framework of Generalized Estimating Equations (GEEs) was performed in order toaccount for correlation.Results confirm that dispersion and correlation are phenomena that cannot be eluded in the estimation of SPFs under penalty of loss of efficiency in estimating model parameters. Generalized Estimating Equations overcome this problem allowing to incorporate together dispersion and temporal correlation when a quasi-Poisson distribution is used for modeling crash data. Moreover, whereas GEE regression is handy (many statistical software packages have already implemented GEE functions), the interest of COM-Poisson regression, because of difficulties in interpreting the model parameters and in arranging COM-Poisson codes, is still limited to the research field.
Original languageEnglish
Pages (from-to)11-23
Number of pages13
JournalModern Applied Science
Volume7
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • General

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