摘要：With increasing population and motorization, Kenya as well as other African countries are faced with a tragic road traffic accidents (RTA). This paper looks at 5-year (2015–2020) data downloaded from National Transport and Safety Authority (NTSA) website, to identify trends and review progress of the traffic accidents in the country. The objective is to assess the prevalence of accidents within affected groups and location to identify trends and generalized causative agency from the reported data. From literature review, research activity focused on RTA in the country is minimal compared to the social significance accidents poses. The data were extracted and classified using Latent Dirichlet Allocation, a machine learning algorithm modelled in Matlab to group reported accident briefs into general categories/topic which are closely related. Four categories were identified as leading causes of fatality in the country: Knocking down victims, hit-and-run, losing control and head on collision. The identified causes point to preventable driver’s errors which agrees with other researchers. From trend analysis, fatalities and injuries have increased by 26% and 46.5%, respectively since January 2015 to January 2020. This paper found that injuries in vulnerable road users: pedestrians, pillion passengers and motorcyclist, has seen a foldfold increment compared to 2015 data. From the discussion, urgent fine-tuning of policing to protect vulnerable road user as well as curb the overly decried driver behavior is needed. The paper recommends fine-tuning of data collection, capturing details of accident that will be useful in modeling and data analysis for future planning.
关键词：Road traffic accidents (RTA) machine learning LDA Kenya
