ANALYSIS OF THE VIABILITY OF APPLYING THE PRINCIPAL COMPONENTS TECHNIQUE IN MULTIVARIATE DATA FROM TRAFFIC ACCIDENTS

Yıl 2018, Cilt: 36 Sayı: 4, 1023 - 1032, 01.12.2018

Maria Lígia Chuerubım Irineu Da Sılva

Öz

The risk factors associated with road accident are directly related to the characteristics of the roadway, the vehicle type and the behavior of the driver, among others. For that reason, such traffic elements are intensively investigated and analysed in the field of road safety. Among the techniques and methods developed, those based on statistical analysis have demonstrated a high degree of susceptibility to the problem and have been applied in several studies of traffic accidents. In this perspective, this work presents a theoretical and applied discussion of the technique of Principal Component Analysis (PCA), in the study of road accidents. The main objective is to contribute to the discussion and theoretical foundation of the statistical techniques, used in the multivariate analysis of highway databases, generated by roadway concessionaries. The database used for this study is from the Dom Pedro I Highway, located in the urban area of the Campinas city in Brazil, during the period of four years from 2009 to 2012.

Anahtar Kelimeler

Principal Component Analysis (PCA), roadway database, traffic accident.

Kaynakça

• [1] Abdi, H.; Williams, L. J. Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics, 2, 2010.]
• [2] Cattell, R. B. The data box: Its ordering of total resources in terms of possible relational systems. In R. B. Cattell (Ed.), Handbook of multivariate experimental psychology (pp. 67–128). Chicago, IL: Rand-McNally, 1966.
• [3] Cattell, R. B. The grammar of science and the evolution of personality theory. In R. B, 1977.]
• [4] Hotelling, H. Simplifield calculation of principal components. Psychometrika, Williamsburg, v.1, p.27-35, 1936.]
• [5] Hongyu, K.; Sandanielo, V. L. M.; Oliveira Junior, G. J. Principal Component Analysis: theory, interpretations and applications. E&S - Engineering and Science, Volume 1, Edição 5:1, 2015.]
• [6] Johnson, R. A.; Wichern, D. W. Applied multivariate statistical analysis. 4th ed. Upper Saddle River, New Jersey: Prentice-Hall, 1999, 815 p.]
• [7] Mardia, K. V.; Kent, J. T.; Bibby, J. M. 1979. Multivariate Analysis. New York: Academic Press.
• [8] Pearson, K. 1901. On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine [S.l.: s.n.] 2 (6): 559–572.
• [9] Solanas, A.; Manolov, R.; Leiva, D. Retaining principal components for discrete variables. Anuario de Psicología, Facultat de Psicologia, Universitat de Barcelona, vol. 41, nº 1-3, 33-50, 2011.
• [10] Wilks, D. S. Statistical Methods in the Atmospheric Sciences, 2nd Ed. v. 91 p. 463-507, 2006.