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
