Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction
Paper i proceeding, 2010

The extent to which accident severity can be predicted from accident-related data collected at a variety of locations is investigated. The 2005 accident dataset brought together by the Republic of Cyprus Police is employed; this dataset comprises 1407 records of 43 continuous and categorical input parameters and a single categorical output parameter representing accident severity. No transformation of the database has been opted for, either by extracting the parameters that are significant for the prediction task or by modifying the records in any way (e.g. via record selection or transformation). Aiming at maximally accurate and efficient prediction, a combination of probabilistic neural networks (PNN's) and decision trees (DT's) is implemented: the simple training and direct operation of the PNN is complemented by the hierarchical, exhaustive and recursive construction of the DT. By training pairs of PNN's on data from the partitions derived from the minimal necessary number of top DT nodes, both efficiency and accident prediction accuracy are maximized.


Tatiani Tampouratzi

Chalmers, Teknisk fysik, Nukleär teknik

D. Souliou

National Technical University of Athens (NTUA)

Panepistimion Pireos

M. Chalikias

Piraeus University of Applied Sciences

Panepistimion Pireos

A. Gregoriades

University of Surrey

European University Cyprus

Proceedings of the International Joint Conference on Neural Networks. 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, 18-23 July 2010

978-142446917-8 (ISBN)


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