Prediction of motorcyclist serious injury in powered two-wheeler to other vehicle urban crash

The aim of this study is to estimate the risk of serious injury for a motorcyclist involved in PTW-to-OV crashes occurred on urban roads. The automotive industry has already developed automatic crash notification algorithms for car occupants and knows well the influence that many crash variables have on the risk of serious injury. A literature review on the prediction of motorcyclist injury severity did not show models based on crash variables, except for crash types and, sometimes, impact speed. Road accidents involving seriously injured riders were selected from the in-depth study of serious road accidents in Florence (InSAFE). Logistic regression techniques were applied to the total of relevant road accidents, in order to find a risk function. The results showed that the risk of serious injury (or major trauma) increased with delta-V of the motorcycle and the other vehicle and personal characteristics, such as the body mass index (BMI). It was found that the capability to discriminate between two states of severity was significant.