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Choice of Functional Form for Independent Variables in Accident Prediction Models

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Języki publikacji
EN
Abstrakty
EN
The development of multivariate statistical models to identify factors that explain systematic variation in accident counts has been an active field of research in the past 20 years. During this period many different models and functional forms have been applied. This study, based on data for national roads in Norway, tests alternative functional forms of the relationship between independent variables and the number of injury accidents. The paper compares six different functional forms (sets of independent variables and specifications of the form of their relationship to accident occurrence) by means of Poisson-lognormal regression. The best model was identified in terms of five goodness of fit measures and a graphical method – the CURE plot (CURE = cumulative residuals). The coefficients estimated for the independent variables were found to vary according to functional form. It is therefore important to compare different functional forms as part of an exploratory analysis when developing accident prediction models.
Rocznik
Strony
51--62
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
  • Wrocław University of Environmental and Life Sciences Department of Mathematics, Poland
Bibliografia
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  • [12] Li, X., Lord, D., Zhang, Y., Xie, Y., 2008. Predicting motor vehicle crashes Support Vector Machine models. Accident Analysis and Prevention 40, 1611-1618.
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  • [14] Lin, D. Y., Wei, L. J., Ying, Z., 2002. Model-Checking Techniques Based on Cumulative Residuals. Biometrics, 58, 1-12.
  • [15] Lord, D., Guikema, S. D., Geedipally, S. R., 2009. Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes. Accident Analysis and Prevention, 41, 1123-1134.
  • [16] Lord, D., Mannering, F., 2010. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A, 44, 291-305.
  • [17] Lord, D., Miranda-Moreno, L. F., 2008. Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: a Bayesian perspective. Safety Science, 46, 751-770.
  • [18] Lord, D., Park, P. Y-J., 2008. Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates. Accident Analysis and Prevention, 40, 1441-1457.
  • [19] Lord, D., Persaud, B. N., 2000. Accident prediction models with and without trend: application of the Generalized Estimating Equations (GEE) procedure. Transportation Research Record, 1717, 102-108.
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  • [22] Ma, J., Kochelman, K. M., Damien, P., 2008. A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. Accident Analysis and Prevention, 40, 964-975.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c947644e-77e2-4fc9-b116-425c464caeec
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