UNRAVELLING ROAD SAFETY: DECODING THE DETERMINANTS OF TRAFFIC ACCIDENTS

Abstract

Road accidents are significantly affecting a shocking number of injuries and fatalities annually. The statistics show a dismal reality: road traffic crashes account for approximately 85% of annual deaths and lead to 90% of disability-adjusted life years lost. This shocking trend not only poses a serious threat to individual lives but also imposes a substantial burden on the economy, overloading healthcare systems and decreasing productivity. In light of these serious concerns, this study adopts a particular analytical approach to investigate the determinants of road traffic accidents using a binary logistic regression model. The research makes use of secondary data collected from road traffic police stations, categorising accidents into fatal and non-fatal cases systematically. Road traffic accident analysis in detail takes into account a wide range of factors that can influence them, which can be categorised under four broad categories: human, road, environmental, and vehicle attributes. Human attributes investigate significant variables with a massive bearing on accident occurrence. The most important factors are the gender and age of the driver, results of alcohol tests, the validity of driving licenses, and overall causes of accidents, which are grouped by types such as speeding, aggressive driving behaviour, and others. It is necessary to learn about such human factors to be able to identify at-risk populations and develop targeted educational and intervention programs. The study also considers the physical attributes of the road environment. This extensive analytical study, study aims to provide insight into the intricate determinants that cause road traffic accidents. The findings will not only enhance our understanding of the complexities surrounding road safety but also offer valuable insights for policymakers and stakeholders seeking to implement targeted interventions and formulate effective road safety strategies