@article{ref1,
title="Modeling road traffic crashes with zero-inflation and site-specific random effects",
journal="Statistical methods and applications",
year="2010",
author="Huang, Hongwei and Chin, Hong Chor",
volume="19",
number="3",
pages="445-462",
abstract="Zero-inflated count models are increasingly employed in many fields in case of &quot;zero-inflation&quot;. 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.<p />",
language="",
issn="1618-2510",
doi="10.1007/s10260-010-0136-x",
url="http://dx.doi.org/10.1007/s10260-010-0136-x"
}