| dc.description.abstract | The road transport industry in Kenya plays a vital role in the life of a majority of its citizens. Many Kenyans utilizes different transport modes to reach their various destinations on a daily basis. Nearly 3000 people are killed on Kenyan roads annually. This translates to approximately 68 deaths per 10,000 registered vehicles, which is 30-40 times greater than in highly motorized countries. Nairobi County has one of the highest road fatality rates in relation to vehicle ownership in Kenya, with an average of 7 deaths from the 35 road crashes that occur each day. Despite the huge burden the major causes of accidents in Nairobi, have not been modeled so as to outline the major causes and their inter-relatedness. Current interventions are sporadic, uncoordinated and less effective despite the huge economic burden exerted by RTAs.
This study sought to explore the major causes that were likely to contribute to road traffic accidents in Nairobi County. This was to be achieved using suitable techniques whose performances were subsequently analyzed. The study utilized accident data between the years 2000-2014 obtained from Nairobi Traffic Police department. Poisson and the negative regression models were used to identify the main risk factors and model that performed better with the traffic data in Nairobi County. The results indicated that the negative-binomial model (R2:0.6691, AIC: 1714.7) outperformed the Poisson model (R2: 0.5991, AIC: 2433.1 ) as on this occasion was concluded as robust model for the prediction of RTAs in Nairobi County. In both models drivers, pedal. Cyclists, pedestrians and passengers significantly contributed to RTAs and thus policy measures should be formulated with them in mind. | en_US |
