DOI:

https://doi.org/10.14483/23448407.16720

Published:

2020-12-04

Issue:

No. 15 (2020)

Section:

Artículo de investigación científica y tecnológica

Data mining techniques for road accidentes

Clustering versus complex netwoks

Authors

  • Maria Lígia Chuerubim Universidade Federal de Uberlândia, UFU, Faculdade de Engenharia Civil, Uberlândia, Minas Gerais, Brasil Escola de Engenharia de São Carlos, USP, São Carlos, São Paulo, Brasil https://orcid.org/0000-0002-2019-9198
  • Alan D.B. Valejo Department of Computing and Mathematics (DCM), FFCLRP, University of São Paulo (USP), Ribeirão Preto, SP, Brazil https://orcid.org/0000-0002-9046-9499
  • George Miguel Farha Diban Universidade Estadual Paulista, UNESP, Faculdade de Engenharia Civil , Bauru, São Paulo, Brasil https://orcid.org/0000-0002-7193-2783
  • Barbara Stolte Bezerra Universidade Estadual Paulista, UNESP, Faculdade de Engenharia Civil , Bauru, São Paulo, Brasil https://orcid.org/0000-0002-8459-4664
  • Irineu da Silva Escola de Engenharia de São Carlos, Departamento de Engenharia de Transportes, USP, São Carlos, São Paulo, Brasil https://orcid.org/0000-0001-5775-6683
  • Bruno Oliveira Lázaro Universidade Federal de Uberlândia, UFU, Faculdade de Engenharia Civil, Uberlândia, Minas Gerais, Brasil https://orcid.org/0000-0003-1667-5216

Keywords:

Road Safety, Clustering, Complex Networks, Decision Rules, Data Mining (en).

Abstract (en)

This work analyses the performance of grouping methods based on complex networks and clusters, in order to identify main road accident groups and risk factors. The research included a balancing step of data classes, used in the classification and extraction process of decision rules applied in each grouping. Then, was possible the assessment and visualization of critical areas of traffic accidents involving victims with material damage, non-fatal and fatal victims. The results pointed out that complex networks present better possibility of generalization for different subsets of data, and higher accuracy in group formation when compared to traditional clustering methods. The use of complex networks aided in the process of acquiring decision rules with higher level of confidence, and higher probability of occurrence.

Abstract (es)

Este trabajo analiza el rendimiento de los métodos de agrupación basados ​​en redes y clústeres complejos, con el fin de identificar los principales grupos de accidentes de tráfico y factores de riesgo. Una investigación incluyó un paso para equilibrar las clases de datos, utilizadas en el proceso de clasificación y extracción de reglas de decisión, aplicadas en cada grupo. Entonces, fue posible evaluar y responder a áreas críticas de accidentes de tránsito, que involucran daños materiales, víctimas no fatales y fatales. Los resultados señalaron que como redes complejas presentan una mejor posibilidad de generalización para diferentes subconjuntos de datos y una mayor precisión en la formación de grupos, en comparación con los métodos tradicionales de agrupación. El uso de redes complejas ayuda en el proceso de adquisición de reglas de decisión con un mayor nivel de confianza y una mayor probabilidad de ocurrencia.

Author Biography

Maria Lígia Chuerubim, Universidade Federal de Uberlândia, UFU, Faculdade de Engenharia Civil, Uberlândia, Minas Gerais, Brasil Escola de Engenharia de São Carlos, USP, São Carlos, São Paulo, Brasil

Possui graduação em Engenharia Cartográfica pela Universidade Estadual Paulista Júlio de Mesquita Filho (2006). Mestrado realizado junto ao Programa de Pós- Graduação em Ciências Cartográficas da UNESP (2007-2009).Tem experiência na área de Geociências, com ênfase em Geodésia, atuando principalmente nos seguintes temas: Posicionamento GNSS, Integração de Redes Geodésicas. De 2009 a 2010, atuou como Analista de Desenvolvimento Fundiário no Programa Cadastro de Terras e Regularização Fundiária no Brasil realizado pelo Ministério do Desenvolvimento Agrário (MDA) em parceria com o Instituto de Terras do Estado de São Paulo (ITESP). De 2010 a 2011 foi Professora de Ensino Básico, Técnico e Tecnológico no Instituto Federal do Espírito Santo (IFES) junto à Coordenadoria de Geomática. Atualmente, é Professora da Faculdade de Engenharia Civil (FECIV) na Universidade Federal de Uberlândia (UFU), além de se dedicar à pesquisas em diferentes àreas das Geociências como Geodésia, Fotogrametria e Sensoriamento Remoto.

References

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BERTON, L.; FALEIROS, T.; VALEJO, A.; VALVERDE-REBAZA, J.; AND LOPES, A. A. RGCLI: Robust Graph that Considers Labeled Instances for Semi-Supervised Learning. Neurocomputing, (2016).
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DE OÑA, J.; LÓPEZ, G.; MUJALLI, R.; CALVO, F. J. Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis and Prevention, v. 51, p. 1–10, 2013.
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LIN, L.; WANG, Q.; SADEK, A. W. Data Mining and Complex Networks Algorithms for Traffic Accident. Transportation Research Record Journal of the Transportation Research Board, January 2014.
NEWMAN, M. E. J. Coauthorship networks and patterns of scientific collaboration. In: Proceedings of the National Academy of Science of the Uninted States, v. 101, n. PNAS’04, p. 5200–5205, 2004.
ROUSSEEUW, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, v. 20, 53–65, 1987.

How to Cite

APA

Chuerubim, M. L., Valejo, A. D., Farha Diban, G. M., Stolte Bezerra , B., da Silva, I. ., and Oliveira Lázaro, B. (2020). Data mining techniques for road accidentes: Clustering versus complex netwoks. UD y la geomática, (15). https://doi.org/10.14483/23448407.16720

ACM

[1]
Chuerubim, M.L. et al. 2020. Data mining techniques for road accidentes: Clustering versus complex netwoks. UD y la geomática. 15 (Dec. 2020). DOI:https://doi.org/10.14483/23448407.16720.

ACS

(1)
Chuerubim, M. L.; Valejo, A. D.; Farha Diban, G. M.; Stolte Bezerra , B.; da Silva, I. .; Oliveira Lázaro, B. Data mining techniques for road accidentes: Clustering versus complex netwoks. U.D. geomatica 2020.

ABNT

CHUERUBIM, Maria Lígia; VALEJO, Alan D.B.; FARHA DIBAN, George Miguel; STOLTE BEZERRA , Barbara; DA SILVA, Irineu; OLIVEIRA LÁZARO, Bruno. Data mining techniques for road accidentes: Clustering versus complex netwoks. UD y la geomática, [S. l.], n. 15, 2020. DOI: 10.14483/23448407.16720. Disponível em: https://revistas.udistrital.edu.co/index.php/UDGeo/article/view/16720. Acesso em: 17 jul. 2024.

Chicago

Chuerubim, Maria Lígia, Alan D.B. Valejo, George Miguel Farha Diban, Barbara Stolte Bezerra, Irineu da Silva, and Bruno Oliveira Lázaro. 2020. “Data mining techniques for road accidentes: Clustering versus complex netwoks”. UD y la geomática, no. 15 (December). https://doi.org/10.14483/23448407.16720.

Harvard

Chuerubim, M. L. (2020) “Data mining techniques for road accidentes: Clustering versus complex netwoks”, UD y la geomática, (15). doi: 10.14483/23448407.16720.

IEEE

[1]
M. L. Chuerubim, A. D. Valejo, G. M. Farha Diban, B. Stolte Bezerra, I. . da Silva, and B. Oliveira Lázaro, “Data mining techniques for road accidentes: Clustering versus complex netwoks”, U.D. geomatica, no. 15, Dec. 2020.

MLA

Chuerubim, Maria Lígia, et al. “Data mining techniques for road accidentes: Clustering versus complex netwoks”. UD y la geomática, no. 15, Dec. 2020, doi:10.14483/23448407.16720.

Turabian

Chuerubim, Maria Lígia, Alan D.B. Valejo, George Miguel Farha Diban, Barbara Stolte Bezerra, Irineu da Silva, and Bruno Oliveira Lázaro. “Data mining techniques for road accidentes: Clustering versus complex netwoks”. UD y la geomática, no. 15 (December 4, 2020). Accessed July 17, 2024. https://revistas.udistrital.edu.co/index.php/UDGeo/article/view/16720.

Vancouver

1.
Chuerubim ML, Valejo AD, Farha Diban GM, Stolte Bezerra B, da Silva I, Oliveira Lázaro B. Data mining techniques for road accidentes: Clustering versus complex netwoks. U.D. geomatica [Internet]. 2020 Dec. 4 [cited 2024 Jul. 17];(15). Available from: https://revistas.udistrital.edu.co/index.php/UDGeo/article/view/16720

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