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|Title:||Modeling road traffic crashes with zero-inflation and site-specific random effects|
Crash prediction model
Zero-inflated count model
|Source:||Huang, H., Chin, H.C. (2010). Modeling road traffic crashes with zero-inflation and site-specific random effects. Statistical Methods and Applications 19 (3) : 445-462. ScholarBank@NUS Repository. https://doi.org/10.1007/s10260-010-0136-x|
|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. © 2010 Springer-Verlag.|
|Source Title:||Statistical Methods and Applications|
|Appears in Collections:||Staff Publications|
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