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Motorcyclists are an integral component of the traffic stream especially, in low-income developing countries like Pakistan. However, motorcyclists lay in the group of vulnerable road users i.e., road users with the least protection, along with pedestrians and cyclists. Therefore, the current study employed the random parameters logit model to identify key risk factors associated with motorcyclist injury severity using three years crash data (2017-2019) for city of Rawalpindi, Pakistan. To calibrate the model, motorcyclist injury severity thresholds are classified as no injury, minor injury, severe injury, and fatal injury. For motorcyclist injury severity analysis, the effects of vehicle crash characteristics, weather conditions, rider attributes and other socio-demographic considerations were primarily considered. The study results showed that severe and fatal injury risk is increased for crashes occurred during weekdays, involving riders aged above 50 years, involving the collision of motorcycles with passenger car and heavy vehicles, involving a female as a pillion rider, and those that occurred due to over speeding. Based on the results obtained from the model, the study suggests several policy implication as strict implementation of traffic regulations such as heavy fines or cancellation of driving license on over speeding, wrong turns, inappropriate passing, running a red light, not wearing helmet etc., instructing the females not to wear loose clothes or trailing scarves while riding the bike as pillion riders. The outcomes are expected to stimulate more interest and discussion regarding motorcycle safety in the country and can be used by city traffic police and highway authorities to boost the road safety.

Investigation of factors influencing motorcyclist injury severity using random parameters logit model with heterogeneity in means and variances