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Predicting the number of accidents at a road junction

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Abstract

We consider a model, within a Bayesian framework, which can be used to predict the number of accidents occurring at a road junction in a given period of time. The predictions are based on measurements of the traffic flows as well as on covariates which describe important features of the junctions. Various approximate and estimative methods, which use Gibbs sampling, posterior normality and Laplace approximations, are considered and compared. Procedures to assess the importance of the different covariates through the use of the Kullback-Leibler measure of divergence are also developed.

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References

  • Aitchison, J. (1975). Goodness of prediction fit.Biometrika, 62:547–554.

    Article  MATH  MathSciNet  Google Scholar 

  • Barnett, V. andWright, D. (1992). Biomedical applications for a generalized linear functional Poisson model.J. Appl. Statist., 19:41–47.

    Google Scholar 

  • Bernardo, J. andSmith, A. (1994).Bayesian Theory. Wiley, Chichester.

    MATH  Google Scholar 

  • Dunsmore, I. andRobson, D. (1997). Prediction in some poisson errors in variables models.Scand. J. Statist., 24:543–554.

    Article  MATH  MathSciNet  Google Scholar 

  • Gaver, D. andO'Muircheartaigh, I. (1987). Robust empirical Bayes analyses of event rates.Technometrics, 29:1–15.

    Article  MATH  MathSciNet  Google Scholar 

  • Gelfand, A. andSmith, A. (1990). Sampling-based approaches to calculating marginal densities.J. Amer. Statist. Ass., 85:398–409.

    Article  MATH  MathSciNet  Google Scholar 

  • Gilks, W. andWild, P. (1992). Adaptive rejection sampling for Gibbs sampling.Appl. Statist., 41:337–348.

    Article  MATH  Google Scholar 

  • Kullback, S. andLeibler, R. (1981). On information and sufficiency.Ann. Math. Statist., 22:79–86.

    MathSciNet  Google Scholar 

  • Magalhães, F. (1997).Prediction in Poisson and other errors in variables models. Ph.D. thesis, University of Sheffield.

  • O'Hagan, A. (1994).Bayesian Inference-Kendalla's Advanced Theory of Statistics, vol. 2B. Edward Arnold, London.

    Google Scholar 

  • Wright, D. andBarnett, V. (1991). Fitting predictive accident models in GLIM with uncertainty in the flow estimates. Transport and Road Research Laboratory Report CR286, Department of Transport, UK.

    Google Scholar 

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Authors and Affiliations

  1. Instituto Politécnico do Porto, CEAUL and Universidade Portucalense, Rua Dr. António Bernardino de Almeida, 541/619, 4200, Porto, Portugal

    Fernando Magalhães

  2. University of Sheffied, The Hicks Building, Sheffield, UK

    Iran R. Dunsmore

Authors
  1. Fernando Magalhães
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  2. Iran R. Dunsmore
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Correspondence to Fernando Magalhães.

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Magalhães, F., Dunsmore, I.R. Predicting the number of accidents at a road junction. Test 12, 153–172 (2003). https://doi.org/10.1007/BF02595817

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  • DOI: https://doi.org/10.1007/BF02595817

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