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The project UR:BAN "Cognitive assistance (KA)" aims at developing future assistance systems providing improved performance in complex city traffic. New state-of-the-art panoramic sensor technologies now allow comprehensive monitoring and evaluation of the vehicle environment. In order to improve protection of vulnerable road users such as pedestrians and cyclists, a particular objective of UR:BAN is the evaluation and prediction of their behaviour and actions. The objective of subproject "WER" is development support by providing quantitative estimates of traffic collisions at the very start and predict potential in terms of optimized accident avoidance and reduction of injury severity. For this purpose an integrated computer simulation toolkit is being devised based on real world accidents (GIDAS as well as video documented accidents), allowing the prediction of potential effectiveness and future benefit of assistance systems in this accident scenario. Subsequently, this toolkit may be used for optimizing the design of implemented assistance systems for improved effectiveness.
In the paper it is investigated to what extend one can extrapolate the detailed accident database GIDAS (German In-Depth Accident Study), with survey area Hanover and Dresden region, to accident behavior in other regions and countries within Europe and how such an extrapolation can be implemented and evaluated. Moreover, it is explored what extent of accident data for the target country is necessary for such an extrapolation and what can be done in situations with sparse and low accident information in a target region. It will be shown that a direct transfer of GIDAS injury outcomes to other regions does not lead to satisfactory results. But based on GIDAS and using statistical decision tree methods, an extrapolation methodology will be presented which allows for an adequate prediction of the distribution of injury severity in severe traffic accidents for European countries. The method consists essentially of a separation of accidents into well-described subgroups of accidents within which the accident severity distribution does not vary much over different regions. In contrast the distribution over the various subgroups of accidents typically is rather different between GIDAS and the target. For the separation into the subgroups meaningful accident parameters (like accident type, traffic environment, type of road etc.) have been selected. The developed methodology is applied to GIDAS data for the years 1999-2012 and is evaluated with police accident data for Sweden (2002 to 2012) and the United Kingdom (2004 to 2010). It is obtained that the extrapolation proposal has good to very good predictive power in the category of severe traffic accidents. Moreover, it is shown that iterative proportional fitting enables the developed extrapolation method to lead to a satisfactory extrapolation of accident outcomes even to target regions with sparse accident information. As an important potential application of the developed methodology the a priori extrapolation of effects of (future) safety systems, the operation of which can only be well assessed on the basis of very detailed GIDAS accident data, is presented. Based on the evaluation of the presented extrapolation method it will be shown that GIDAS very well represents severe accidents, i.e. accidents with at least one severely or fatally injured person involved, for other countries in Europe. The developed extrapolation method reaches its limits in cases for which only very little accident information is available for the target region.