Abstract:
Globally, approximately 3700 people die daily in RTAs, resulting in 50
million injuries or disabilities, and 1.35 million deaths annually, contributing to
3% of the GDP of most underdeveloped countries in the world. According to the
NHTSA, factors related to human contribute approximately 94% of all RTAs.
Accident analysis can be approached by two methods, the Person-Based and
System-Based approaches. The Person-Based method concentrates on unsafe acts
such as errors and violations committed by users due to abnormal processes such
as poor motivation, restlessness, forgetfulness, inattention, and negligence. Whilst,
the System-Based Approach recognizes that human errors are inevitable,
regardless of facility quality, where errors are considered as consequences rather
than causes. A lot of work has been done on person-based approach as compared
to system-based approach. Our study has focused on System-Based approach by
utilizing a holistic approach that combines HFACS, FTA and Machine Learning
models to achieve a good understanding of the intricate interplay among human
factors that assist in predicting accidents.
