Abstract

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.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.5753/eniac.2019.9290
https://academic.microsoft.com/#/detail/3011928825
Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.5753/eniac.2019.9290
Licence: CC BY-NC-SA license

Document Score

0

Views 0
Recommendations 0

Share this document

Keywords

claim authorship

Are you one of the authors of this document?