Modeling road traffic crashes with zero-inflation and site-specific random effects

    Authors

    H. L. Huang;H. C. Chin

    Comments

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    Abbreviated Journal Title

    Stat. Method. Appl.

    Keywords

    Traffic safety; Crash prediction model; Bayesian inference; Zero-inflated count model; Random effects; POISSON REGRESSION-MODELS; MOTOR-VEHICLE CRASHES; ACCIDENT FREQUENCIES; BAYESIAN-ANALYSIS; EXCESS ZEROS; COUNT DATA; DENSITIES; Statistics & Probability

    Abstract

    Zero-inflated count models are increasingly employed in many fields in case of "zero-inflation". In modeling road traffic crashes, it has also shown to be useful in obtaining a better model-fitting when zero crash counts are over-presented. However, the general specification of zero-inflated model can not account for the multilevel data structure in crash data, which may be an important source of over-dispersion. This paper examines zero-inflated Poisson regression with site-specific random effects (REZIP) with comparison to random effect Poisson model and standard zero-inflated poison model. A practical and flexible procedure, using Bayesian inference with Markov Chain Monte Carlo algorithm and cross-validation predictive density techniques, is applied for model calibration and suitability assessment. Using crash data in Singapore (1998-2005), the illustrative results demonstrate that the REZIP model may significantly improve the model-fitting and predictive performance of crash prediction models. This improvement can contribute to traffic safety management and engineering practices such as countermeasure design and safety evaluation of traffic treatments.

    Journal Title

    Statistical Methods and Applications

    Volume

    19

    Issue/Number

    3

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    445

    Last Page

    462

    WOS Identifier

    WOS:000281401700008

    ISSN

    1618-2510

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