Neural network and clustering techniques for tractor accidents on highways in the south-east of Brazil - Ximenes Siqueira Macedo, v (Revista Ciência Agronômica, 2021)
Until recently, accident indicators were analysed separately due to the methods employed, however, the joint use ofneural networks and clustering techniques has proven to be an excellent tool for analysing how accidents occur. As such, the aim ofthis 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 weretime, type of accident, cause of accident, weather conditions, condition of the victims, road layout and federated state. The use ofneural networks was based on self-organising maps (SOM), hierarchical clus tering employing dendrograms, and non-hierarchicalclustering 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 withfatalities. It proved possible to analyse the factors that led to the accident, together with its consequences. Machine traffi c duringperiods of low natural light on straight roads caused rear-end collisions, with casualties and fatalities.