Complex event post processing for traffic accidents

In this paper, we describe a framework for an expert system that tries to predict effects of an accident based on past data using supervised learning employing artificial neural networks. For this purpose, sensory data events are post processed in order to generate a reasonable mapping between input and output parameters in case an event is detected automatically or manually. The framework is intended to be used to take actions for reducing the effects of the accident on traffic congestion and to inform necessary parties to intervene in a timely fashion.