Title :
Complex event post processing for traffic accidents
Author :
Ogrenci, Arif Selcuk
Author_Institution :
Kadir Has Univ., Istanbul, Turkey
Abstract :
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.
Keywords :
expert systems; learning (artificial intelligence); neural nets; road accidents; road traffic; traffic engineering computing; accident prediction effects; artificial neural networks; complex event post processing; expert system; reasonable mapping; sensory data events; supervised learning; traffic accidents; traffic congestion;
Conference_Titel :
Computational Intelligence and Informatics (CINTI), 2012 IEEE 13th International Symposium on
Conference_Location :
Budapest
Print_ISBN :
978-1-4673-5205-5
Electronic_ISBN :
978-1-4673-5210-9
DOI :
10.1109/CINTI.2012.6496787
