Please use this identifier to cite or link to this item:
Title: Modeling road traffic crashes with zero-inflation and site-specific random effects
Authors: Huang, H.
Chin, H.C. 
Keywords: Bayesian inference
Crash prediction model
Random effects
Traffic safety
Zero-inflated count model
Issue Date: 2010
Citation: 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.
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
ISSN: 16182510
DOI: 10.1007/s10260-010-0136-x
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 4, 2022


checked on Oct 4, 2022

Page view(s)

checked on Oct 6, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.