Comparison of the Effectiveness of Regression Models for the Number of Road Accident Injuries

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Published: Dec 27, 2023
Keywords:
Count data Injury Maximum likelihood Overdispersion Road accident

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Wikanda Phaphan
Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand, Research Group in Statistical Learning and Inference, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Nutnaree Sangnuch
Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Janjira Piladaeng
Department of Mathematics, Faculty of Science, Burapha University, Chon Buri 20131, Thailand

Abstract

This article aimed to compare the effectiveness of regression models using data on the number of road traffic injuries from the Injury Surveillance System of the Thai Department of Disease Control between 2018 and 2022. The regression models used in this study for the count data include the Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial, and Conway-Maxwell Poisson models. These were compared to find a suitable regression model to predict the number of road traffic injuries. The results show that the negative binomial regression model provides an appropriate regression equation for predicting the number of road traffic injuries because it gives the lowest Akaike information criterion (AIC). Moreover, this model can still be used as a preliminary tool for predicting the number of road accident injuries since it does not rely on many independent variables.

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How to Cite
Phaphan, W., Sangnuch, N., & Piladaeng, J. (2023). Comparison of the Effectiveness of Regression Models for the Number of Road Accident Injuries. Science & Technology Asia, 28(4), 54–66. Retrieved from https://ph02.tci-thaijo.org/index.php/SciTechAsia/article/view/249723
Issue
Vol.28 No.4 (October-December 2023)
Section
Physical sciences
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Author Biographies

Wikanda Phaphan, Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand, Research Group in Statistical Learning and Inference, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand

 

 

Nutnaree Sangnuch, Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand

 

 

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