Modeling random effect and excess zeros in road traffic accident prediction
Data(s) |
2006
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Resumo |
Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended. |
Formato |
application/pdf |
Identificador | |
Relação |
http://eprints.qut.edu.au/51216/1/Modeling_Random_effect_and_Excess_Zero_in_Road_Traffic_Accident.pdf Haque, Md. Mazharul, Chin, Hoong Chor, & Huang, Helai (2006) Modeling random effect and excess zeros in road traffic accident prediction. In 19th KKCNN Symposium on Civil Engineering, Kyoto, Japan. |
Direitos |
Copyright 2006 [please consult the author] |
Fonte |
Centre for Accident Research & Road Safety - Qld (CARRS-Q); School of Civil Engineering & Built Environment; Science & Engineering Faculty |
Palavras-Chave | #010401 Applied Statistics #090507 Transport Engineering #Poisson regression model #Negative Binomial model #Random effect model |
Tipo |
Conference Paper |