On the Analysis of Machine Learning Classifiers to Detect Traffic Congestion in Vehicular Networks

  • Lucas de Carvalho Universidade Federal de São João del-Rei
  • Maycon da Silva Universidade Federal de São João del-Rei
  • Edimilson dos Santos Universidade Federal de São João del-Rei
  • Daniel Guidoni Universidade Federal de São João del-Rei

Resumo


Problems related to traffic congestion and management have become common in many cities. Thus, vehicle re-routing methods have been proposed to minimize the congestion. Some of these methods have applied machine learning techniques, more specifically classifiers, to verify road conditions and detect congestion. However, better results may be obtained by applying a classifier more suitable to domain. In this sense, this paper presents an evaluation of different classifiers applied to the identification of the level of road congestion. Our main goal is to analyze the characteristics of each classifier in this task. The classifiers involved in the experiments here are: Multiple Layer Neural Network (MLP), K-Nearest Neighbors (KNN), Decision Trees (J48), Support Vector Machines (SVM), Naive Bayes and Tree Augment Naive Bayes.

Palavras-chave: machine learning, classifiers, traffic congestion, vehicular networks

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Publicado
15/10/2019
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CARVALHO, Lucas de; SILVA, Maycon da; SANTOS, Edimilson dos; GUIDONI, Daniel. On the Analysis of Machine Learning Classifiers to Detect Traffic Congestion in Vehicular Networks. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 274-285. DOI: https://doi.org/10.5753/eniac.2019.9290.