Multidimensional MCA correspondence model supporting intelligent transport management

The article presents a multiple model using the multiple correspondence analysis MCA in the task of intelligent transport management. It is an exploratory technique of quality data analysis which facilitates graphic presentation of variables in low dimensional space. The model using multiple correspondence analysis looms relatively easily interpretable results. The article will present a data reduction technique. MCA is the method of co-occurrence of categories of variables which are measurable on a nominal scale. Graphic development of variables and determination of the relations among them will also be presented. Thus making it possible to obtain a map of points using the multiple correspondence analysis and identification of occurring concentrations of categories of variables which support use of telematic tools in intelligent transport management.