Predictors of Human Death by Road Traffic Crashes in Bahir Dar City, North Western Ethiopia; A Count Data Analysis Regression Model

Metadel Azeze,Legesse Kassa Debusho,Endalew Tesfa,Awoke Seyoum

doi:10.11648/j.ijtam.20200606.12

Abstract

Road traffic crashes are a major socio-economic and public health problem, affecting all people of the world and Ethiopia is a country with a very large number of traffic crashes and fatality rate. This study has major objective of assessing the predictors of road traffic accident in Bahir Dar city, Ethiopia and identifies factors that contribute to the occurrence of road traffic crashes that leads human death. Data regarding to the number of deaths per road traffic crash were obtained from Bahir Dar city administration traffic police office for a two year period from July 2015-June 2017. In this study we applied six count models namely Poisson, negative binomial, generalized Poisson, zero inflated Poisson, zero-inflated negative binomial and zero inflated generalized Poisson regression models. Based on different models comparison criteria, e.g. AIC, log likelihood and Vuong test ZIGP regression model provides more appropriate fit to the number of human death per road traffic crashes data considered in this study. Sex, age, driving under alcohol, fatigue, not give priority, days of weeks, road condition, overloading, over speeding, and type of accident were found to be statistically significant predictors of human death due to road traffic crash.

Highlights

IntroductionRoad traffic crash is a collision or similar incident involving a moving vehicle, resulting in property damage, personal death or injury [2]
Comparison of ZINB versus negative binomial (NB) models, the calculated value of the Vuong test is 6.168, revealed that the ZINB model was preferred to NB regression model and in the third case, the comparison zero-inflated generalized Poisson (ZIGP) versus generalized Poisson (GP) models, the statistic value of Vuong test is 4.15, and revealed that the ZIGP model was preferred to GP regression model
It is possible to conclude that ZIGP model was more appropriate than the ZIP and ZINB models to fit road traffic crashes dataset

Summary

Introduction

Road traffic crash is a collision or similar incident involving a moving vehicle, resulting in property damage, personal death or injury [2]. Road traffic crash is an unexpected phenomenon that occurs as a result of the use or operation of vehicles including bicycles and handcarts on the public highways and roads. A road traffic crash is defined as any vehicle accident occurring on a public highway. It includes collisions between vehicles with vehicles, vehicles with animals, vehicles with pedestrians, or vehicles with fixed obstacles. Accidents may be fatal, resulting in deaths of the road users (passengers, drivers or pedestrians), or minor when it is not severe enough as to cause substantial hardship [4]. In Africa over 80% of goods and people are transported by roads [6], and in Ethiopia road transport accounts for over 90% of all the inter-urban freight and passenger movements in the country [7]

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