Analysing Motorcycle Injuries on Arterial Roads in Bali using a Multinomial Logit Model

This paper aims to investigate the influence of accident related factors on motorcycle injuries on 2 arterial roads in Bali. Multinomial logit (MNL) models are estimated considering 3 severity classes such as slight injury, serious injury, and fatal injury as response variables using local police data as explanatory variables. The analysis shows that there are 4 variables associated with motorcycle injuries. Sideswipe accidents involving motorcyclists were 51.7% less likely resulting in serious injuries than slight injuries. In addition, motorcycles collided with other vehicle(s), either motorist/motorcyclist failed to yield and motorcycle at fault were 89.1%, 60.7%, and 44%, respectively less likely resulting in fatal injuries than slight injuries. Probability analysis shows that a change in 1% of these variables could influence motorcycle injuries between 33% and 34%.