Uma análise da qualidade dos dados relativos aos boletins de ocorrências das rodovias federais brasileiras para o processo de Mineração de Dados
Resumo
O artigo apresenta um estudo relativo à aplicação do processo de Mineração de Dados nos dados de ocorrências em rodovias federais, gerados pela Polícia Rodoviária Federal, em 2012. O objetivo desse estudo é analisar a viabilidade da aplicação do processo sobre os dados para identificar associações entre variáveis relacionadas aos acidentes de trânsito em todas as rodovias federais brasileiras. Apresentamos neste trabalho as principais dificuldades encontradas na aplicação do processo, os resultados obtidos utilizando os algoritmos de aprendizado PART e Apriori, e descrevemos os trabalhos futuros a serem realizados com base neste estudo.
Palavras-chave:
governo eletrônico
Referências
Barrat, A., Barthélemy M., Pastor-Satorras R., Vespignani, A. (2004) The architecture of Complex Weighted Networks, Proceedings of the National Academy of Sciences, March 16, vol. 101,n. 11, pp 3747-3752
Balijepalli, Chandra, Oppong, Olivia (2014) Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas, Journal of Transport Geography, 39, pp. 145-155.
Borgatti, Stephen P. (2005) Centrality and network flow, Social networks ,27, pp. 55- 71.
Chen, Bi Yu, Lam Willian H. K., Sumalee, A., Li, Qingquan, Li, Zhi-Chun (2012) Vulnerability analysis for large-scale and congested road networks with demand uncertainty, Transportation Research Part A: Policy and Practice 46.3, pp. 501-516
Easley, D., Jon K. (2010) “Networks, Crowds, and Markets”, Vol. 8, Cambridge:Cambridge University Press.
Ferber, C., Berche, B., Holovatch, T., Holovatch Yu. (2012) A tale of Two Cities Vulnerabilities of the London and Paris Transit Networks, J Transp. Secur. 5. 199/216.
Hagberg, A., Swart, P., Chult, D. (2008) “Exploring Network Structure, Dynamics, and Function Using NetworkX”. in Proceedings of the 7th Python in Science Conference (SciPy2008), pp. 11-15.
Jenelius, E., Mattsson L-G. (2015) Vulnerability and resilience of transport systems — A discussion of recent research, Transportation Research Part 4, 81, pp. 16-34.
Joanes, D. N., Gill, C. A. (1998). Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician) 47, pp. 183-189.
Latora, V., Marchiori, M. (2001) Efficient behavior of small-world networks. Physical review letters 87.19, pp.198701.
Nist (2003), “Engineering Statistics Hanbook”, https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
Pearson, M., Sagastuy, J., Samaniego, S. (2017) “Traffic Flow Analysis Using Uber Movement Data”, htts:// snap.stanford.edu/class/projects/
Saramãki, J., Kivel, M., Onnela, J-P., Kaskil, K., Kertész, J. (2007) Generalizations of the Clustering Coefficient to Weighted Complex Networks, Physical Review E, 75.2,pp. 027105.
Sebastiani, M., Luders R., Fonseca K. (2016) “Evaluating electric bus operation for a real-world BRT public transportation using simulation optimization,” IEEE Trans. Intel. Transp. Sys., vol. 99, pp. 1-10.
Uber (2019) “Uber Movement”, https://movement.uber.com/
Vonu P., Tang L., Vassilakis W. (2011) “Spatio-temporal effects of bus arrival time information,” in Proceedings of the 4th ACM SIGSPATIAL International Workshopon Computational Transportation Science, CTS "11, (New York, NY, USA), pp. 6-11.
Balijepalli, Chandra, Oppong, Olivia (2014) Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas, Journal of Transport Geography, 39, pp. 145-155.
Borgatti, Stephen P. (2005) Centrality and network flow, Social networks ,27, pp. 55- 71.
Chen, Bi Yu, Lam Willian H. K., Sumalee, A., Li, Qingquan, Li, Zhi-Chun (2012) Vulnerability analysis for large-scale and congested road networks with demand uncertainty, Transportation Research Part A: Policy and Practice 46.3, pp. 501-516
Easley, D., Jon K. (2010) “Networks, Crowds, and Markets”, Vol. 8, Cambridge:Cambridge University Press.
Ferber, C., Berche, B., Holovatch, T., Holovatch Yu. (2012) A tale of Two Cities Vulnerabilities of the London and Paris Transit Networks, J Transp. Secur. 5. 199/216.
Hagberg, A., Swart, P., Chult, D. (2008) “Exploring Network Structure, Dynamics, and Function Using NetworkX”. in Proceedings of the 7th Python in Science Conference (SciPy2008), pp. 11-15.
Jenelius, E., Mattsson L-G. (2015) Vulnerability and resilience of transport systems — A discussion of recent research, Transportation Research Part 4, 81, pp. 16-34.
Joanes, D. N., Gill, C. A. (1998). Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician) 47, pp. 183-189.
Latora, V., Marchiori, M. (2001) Efficient behavior of small-world networks. Physical review letters 87.19, pp.198701.
Nist (2003), “Engineering Statistics Hanbook”, https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm
Pearson, M., Sagastuy, J., Samaniego, S. (2017) “Traffic Flow Analysis Using Uber Movement Data”, htts:// snap.stanford.edu/class/projects/
Saramãki, J., Kivel, M., Onnela, J-P., Kaskil, K., Kertész, J. (2007) Generalizations of the Clustering Coefficient to Weighted Complex Networks, Physical Review E, 75.2,pp. 027105.
Sebastiani, M., Luders R., Fonseca K. (2016) “Evaluating electric bus operation for a real-world BRT public transportation using simulation optimization,” IEEE Trans. Intel. Transp. Sys., vol. 99, pp. 1-10.
Uber (2019) “Uber Movement”, https://movement.uber.com/
Vonu P., Tang L., Vassilakis W. (2011) “Spatio-temporal effects of bus arrival time information,” in Proceedings of the 4th ACM SIGSPATIAL International Workshopon Computational Transportation Science, CTS "11, (New York, NY, USA), pp. 6-11.
Publicado
27/05/2014
Como Citar
COSTA, Jefferson de J.; BERNARDINI, Flavia Cristina; LIMA, Thiago J. B. de; VITERBO, José.
Uma análise da qualidade dos dados relativos aos boletins de ocorrências das rodovias federais brasileiras para o processo de Mineração de Dados. In: WORKSHOP DE COMPUTAÇÃO APLICADA EM GOVERNO ELETRÔNICO (WCGE), 6. , 2014, Londrina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 9-16.
ISSN 2763-8723.
