Motorcycle riders are one of the most vulnerable road users. Annually, on estimate 6000 people are killed in motorcycle accidents in the former 15 EU countries. The objective of this research was to investigate and analyze the main aspects and causes of this vulnerability and the accidents in general. For this aim around 70 accidents in The Netherlands were investigated in the framework of an international research program (MAIDS). Also a control group of motorcycles with riders was investigated so that exposure could be taken into account. An important result is that human failure is in 82% of the cases the main cause of the accident, in 52% this is due the other vehicle driver. Perception and decision failures are the most common failures. The most injuries are caused by the environment but they are typically only less severe (AIS1). Injuries caused by the car (front and side) are typically severe injuries (AIS4+). Previous convictions of the MC rider seem to be related to the chance to get involved in an accident. It was shown that the Dutch and the total MAIDS accident sample are comparable.
Internationally, the need is expressed for harmonized traffic accident data collection (PSN, PENDANT, etc.). Together with this effort of harmonization, traffic accident investigation moves more and more in the direction of accident causation. As current methods only partly address these needs, a new method was set up. The main characteristics of this method are: • Accident/injury causation (associated) factors can objectively be identified and quantified, by comparison with exposure information from a normal population. • All relevant accident and exposure data can be included: human-, vehicle-, and environmental related data for the pre-crash, crash and postcrash situation (the so-called Haddon matrix). The level of detail can be chosen depending on interest and/or budget, which makes the method very flexible. In this paper the accident collection and control group method are presented, including some of the achieved results from a pilot study on 30 truck accidents and 30 control locations. The data were analyzed by using cross-tabulations and classification-tree analysis. The method proved useful for the identification of statistically significant causational aspects.