Neural network and clustering techniques for tractor accidents on highways in the south-east of Brazil

Abstract

Until recently, accident indicators were analysed separately due to the methods employed, however, the joint use of
neural networks and clustering techniques has proven to be an excellent tool for analysing how accidents occur. As such, the aim of
this study was to use neural networks and cluster analysis on accident indicators involving tractors on federal highways in the southeast of Brazil. A total of 496 incidents were analysed between 2007 and 2016. The indicators for the accidents under evaluation were
time, type of accident, cause of accident, weather conditions, condition of the victims, road layout and federated state. The use of
neural networks was based on self-organising maps (SOM), hierarchical clus tering employing dendrograms, and non-hierarchical
clustering employing the k-means coeffi cient. Using these techniques, it was possible to divide the incidents into 18 accident groups,
of which 11 were represented by the state of Minas Gerais, one group where casualties were predominant, and one group with
fatalities. It proved possible to analyse the factors that led to the accident, together with its consequences. Machine traffi c during
periods of low natural light on straight roads caused rear-end collisions, with casualties and fatalities.