A self-organizing feature maps and data mining based decision support system for liability authentications of traffic crashes

This study develops a decision support tool for liability authentications of two-vehicle crashes based on generated self-organizing feature maps (SOM) and data mining (DM) models. Factors critical to liability attributions commonly identified theoretically and practically were first selected. Both SOM and DM models were then generated for frontal, side, and rear collisions of two-vehicle crashes. Appropriateness of all generated models was evaluated and confirmed. Finally, a decision support tool was developed using active server pages. Although with small data size, the decision support system was considered capable of giving reasonably good liability attributions and references on given cases.