Modelling road accidents : an empirical comparison of algorithms for learning Bayesian networks.

Author(s)
Del Missier, F. Fum, D. & Pani, B.
Year
Abstract

The paper presents the first results of the BayWay (Bayesian Roadway) project by providing an empirical comparison of some algorithms for intelligent data mining and Bayesian network learning on artificial road accident datasets. The theoretical motivations of the project are outlined, the problems involved in the construction of statistical models of road accidents dealt with, and a hybrid approach (MIDA) introduced. The machine learning algorithms and the artificial datasets used in the comparison are then presented. The methodology and the metrics utilised in the experiment are illustrated and the main results are described. The paper ends by drawing some general conclusions about the techniques to be used for mining road accident datasets.

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Publication

Library number
C 23079 (In: C 22454 CD-ROM) /80 /81 / ITRD E115164
Source

In: From vision to reality : proceedings of the 7th World Congress on Intelligent Transportation Systems ITS, Turin, Italy, 6-9 November 2000, 8 p., 9 ref.

Our collection

This publication is one of our other publications, and part of our extensive collection of road safety literature, that also includes the SWOV publications.