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dc.contributor.advisor Tessera, A.
dc.contributor.advisor Yibas, N. Mphekgwana, Modupi Peter 2021-09-29T08:14:38Z 2021-09-29T08:14:38Z 2020
dc.description Thesis (M.Sc. (Statistics)) -- University of Limpopo, 2020 en_US
dc.description.abstract Background: Death and economic losses due to road traffic accidents (RTA) are huge global public health and developmental problems and need urgent attention. Each year nearly 1.24 million people die and millions suffer various forms of disability as a result of road accidents. This puts road traffic injuries (RTIs) as the eighth leading cause of death globally and RTIs are set to become the fifth leading cause of death worldwide by the year 2030 unless urgent actions are taken. Aim: In this paper, we investigate factors that contribute to road traffic deaths (RTDs) in the Limpopo province of South Africa using models such as the generalized linear models (GLM) and zero inflated models. Methods: The study was based on retrospective data that comprised of reports of 18,029 road traffic accidents and 4,944 road traffic deaths over the years 2009 – 2015. Generalized linear modelling and zero-inflated models were used to identify factors and determine their relationships to RTDs. Results: The data was split into two categories: deaths that occurred during holidays and those that occurred during non-holiday periods. It was found that the following variables, namely, Monday, human actions, vehicle conditions and vehicle makes, were significant predictors of RTDs during holidays. On the other hand, during non-holiday periods, weekend, Tuesday, Wednesday, national road, provincial road, sedan, LDV, combi and bus were found to be significant predictors of road traffic deaths. Conclusion: GLM techniques, such as the standard Poisson regression model and the negative binomial (NB) model, did little to explain the zero excess, therefore, zero-inflated models, such as zero-inflated negative binomial (ZINB), were found to be useful in explaining excess zeros. Recommendation: The study recommends that the government should make more human power available during the festive seasons, such as the December holidays, and over weekends. en_US
dc.format.extent vii, 84 leaves en_US
dc.language.iso en en_US
dc.relation.requires PDF en_US
dc.subject Poisson en_US
dc.subject ZIP en_US
dc.subject ZINB en_US
dc.subject NB en_US
dc.subject Accidents en_US
dc.subject Deaths en_US
dc.subject RTAs en_US
dc.subject RTDs en_US
dc.subject Zeros en_US
dc.subject.lcsh Linear models (Statistics) en_US
dc.subject.lcsh Traffic fatalities -- South Africa -- Limpopo en_US
dc.subject.lcsh Traffic accident investigation -- South Africa -- Limpopo en_US
dc.subject.lcsh Accidents -- Statistics en_US
dc.title Analysis of road traffic accidents in Limpopo Province using generalized linear modelling en_US
dc.type Thesis en_US

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