Citation

Dias D, Silva JS, Bernardino A. Informatics (Basel) 2023; 10(1): e17.

Copyright

(Copyright © 2023, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/informatics10010017

PMID

unavailable

Abstract

This work proposes a tool to predict the risk of road accidents. The developed system consists of three steps: data selection and collection, preprocessing, and the use of mining algorithms. The data were imported from the Portuguese National Guard database, and they related to accidents that occurred from 2019 to 2021. The results allowed us to conclude that the highest concentration of accidents occurs during the time interval from 17:00 to 20:00, and that rain is the meteorological factor with the greatest effect on the probability of an accident occurring. Additionally, we concluded that Friday is the day of the week on which more accidents occur than on other days. These results are of importance to the decision makers responsible for planning the most effective allocation of resources for traffic surveillance.

Language: en

Keywords

classical methods; deep neural networks; risk prediction; road accidents; supervised classification