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

DOI: https://doi.org/10.5753/eniac.2019.9290

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

Referências

Araujo, G., Queiroz, M., Duarte-Figueiredo, F., Tostes, A. and Loureiro, A. (2014) “Cartim: A proposal toward identification and minimization of vehicular traffic congestion for vanet”, In Computers and Communication (ISCC), IEEE Symposium on, p. 1–6.

Bila, C., Sivrikaya, F., Khan, M. A. and Albayrak, S. (2017) “Vehicles of the future: A survey of research on safety issues”, IEEE Transactions on Intelligent Transportation Systems 18 (5), p. 1046–1065.

Cirelo, M. C. and Cozman, F. G. (2005) “Aprendizado semi-supervisionado de classificadores bayesianos utilizando testes de independência”, EPUSP: São Paulo.

Cunha, F., Villas, L., Boukerche, A., Maia, G., Viana, A., Mini, R. A. and Loureiro, A. A. (2016) “Data communication in vanets: Protocols, applications and challenges”, Ad Hoc Networks 44 (Supplement C), p. 90 – 103.

Demšar, J. (2006) “Statistical Comparisons of Classifiers over Multiple Data Sets”, Journal of Machine Learning Research, p. 1-30.

Djahel, S., Doolan, R., Muntean, G. M. and Murphy, J. (2015) “A communicationsoriented perspective on traffic management systems for smart cities: Challenges and innovative approaches”, IEEE Communications Surveys Tutorials 17 (1), p. 125– 151.

Friedman, N., Geiger, D. and Goldszmidt, M. (1997) “Bayesian network classifiers”, Machine learning. Springer, v. 29, n. 2-3, p. 131–163.

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. H. (2009) “The WEKA Data Mining Software: An Update”, SIGKDD Explorations, vol. 11, p. 10-18.

Meneguette, R. I., Fillho, G. P. R., Bittencourt, L. F., Ueyama, J. and Villas, L. A. (2016) “A solution for detection and control for congested roads using vehicular networks”, IEEE Latin America Transactions. IEEE, v. 14, n. 4, p. 1849–1855.

Russel, S. and Norvig, P. (2013) “Artificial Inteligence”. Rio de Janeiro: Elsevier.

Souza, A. M., Guidoni, D., Botega, L. C. and Villas, L. A. (2016) “CO-OP: Uma solução para a detecção, classificação e minimização de congestionamentos de veículos utilizando roteamento cooperativo”, In XXXIV Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Bahia, Brasil.

Van den Haak, W. P., Rothkrantz, L. J. M. and Wiggers, P. (2010) “Modeling traffic information using bayesian networks”, Transactions on Transport Sciences, v. 3, n. 3, p. 129–136.

Wolpert, D. H. (1996) “The Lack of A Priori Distinctions Between Learning Algorithms”, Neural Computation, v. 8, n. 7, p. 1341-1390.