AbstractPrincipal component analysis (PCA) is used to analyze one-year traffic, emission and meteorological data for an urban intersection in the Delhi. The 1997 data include meteorological, traffic and emission variables. In urban intersections the complexities of site, traffic and meteorological characteristic may result in a high cross correlation among the variables. In such situations, PCA can provide an independent linear combination of the variables. Here it is used to analyze 1, 8 and 24 h average emission, traffic and meteorological data. It shows that four principal components for the 24 h average have the highest loadings for traffic and emission variables with a strong correlation between them. PC loadings for the 1 and 8 h data indicate the least variation among them.
Principal component analysis of urban traffic characteristics and meteorological data
Transportation Research Part D: Transport and Environment ; 8 , 4 ; 285-297
2003-01-01
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Principal component analysis of urban traffic characteristics and meteorological data
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