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An analysis of motorcycle injury and vehicle damage severity using ordered probit models

journal contribution
posted on 04.09.2009, 10:36 by Mohammed Quddus, Robert B. Noland, Hoong Chor Chin
Problem: Motorcycles constitute about 19% of all motorized vehicles in Singapore and are generally overrepresented in traffic accidents, accounting for 40% of total fatalities. Method: In this paper, an ordered probit model is used to examine factors that affect the injury severity of motorcycle accidents and the severity of damage to the vehicle for those crashes. Nine years of motorcycle accident data were obtained for Singapore through police reports. These data included categorical assessments of the severity of accidents based on three levels. Damage severity to the vehicle was also assessed and categorized into four levels. Categorical data of this type are best analyzed using ordered probit models because they require no assumptions regarding the ordinality of the dependent variable, which in this case is the severity score. Various models are examined to determine what factors are related to increased injury and damage severity of motorcycle accidents. Results: Factors found to lead to increases in the probability of severe injuries include the motorcyclist having non- Singaporean nationality, increased engine capacity, headlight not turned on during daytime, collisions with pedestrians and stationary objects, driving during early morning hours, having a pillion passenger, and when the motorcyclist is determined to be at fault for the accident. Factors leading to increased probability of vehicle damage include some similar factors but also show some differences, such as less damage associated with pedestrian collisions and with female drivers. In addition, it was also found that both injury severity and vehicle damage severity levels are decreasing over time.



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QUDDUS, M.A., NOLAND, R.B. and CHIN, H.C., 2002. An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Journal of Safety Research, 33(4), pp. 445-462


© National Safety Council and Elsevier


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