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

Deivielison Ximenes Siqueira Macedo, Leonardo de Almeida Monteiro, Viviane Castro dos Santos, Jefferson Auteliano Carvalho Dutra, José Wally Mendonça Menezes


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 south-east 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 clustering employing dendrograms, and non-hierarchical clustering employing the k-means coefficient. 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 traffic during periods of low natural light on straight roads caused rear-end collisions, with casualties and fatalities.


SOM networks. k-means. Safety, Incidents. Agricultural machinery.

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