Análise da importância das variáveis intervenientes nos acidentes de trânsito em interseções urbanas utilizando redes neurais artificiais.

Abstract

The technological development has generated great amount of potential data bases in order to supply information for several aspects related to road safety. However, the transformation of these great amount of data in useful information for the technicians, public managers and the population in general, requests the modeling and the treatment of these data using some analysis tools that allow a visualization of the results in form easily understandable. This work presents a new methodology of traffic accidents analysis based in the artificial neural network (ANN). ANN can be very useful for organizations, public or particular, mainly to those that propose to understand the phenomena of the traffic in order to looking for solutions integrated to several areas such as education, engineering and fiscalization. This research had as general objective to identify the patterns of traffic accidents that happened at urban intersections. The data of accidents that happened in the period from 2000 to 2003, in the city of São Carlos were used for the case study, in order to subsidize the elaboration and the evaluation of public policies of traffic accidents reduction and specially the reduction of accident severity. The study explores the assumption that different accident types are related to different patterns. The patterns obtained by ANN showed that there are significant differences in the factors that can affect the different types of accidents. The knowledge of the patterns of each accident type is essential to develop actions corrective or preventive road safety's improvement in order to avoid undesirable effects when these actions are implemented. However, the comparison between the patterns of the different types of accidents was difficult due to the heterogeneity of the situations and the different elements that compose the road environment that can affect the occurrence of the accident.