Principal Component Analysis of the Potential for Increased Rail Competitiveness in East-Central Europe

Abstract

Increased rail competitiveness has been the objective of many countries around the world, including member states of the EU. Although railway market liberalization has always been accompanied by high expectations of increased efficiency and competitiveness, the overall impact of such decisions can be considered controversial. This paper aims to contribute to the scientific debate by conducting a factor analysis of some East-Central European countries from the aspect of rail freight competitiveness. Since many highly correlated factors influence competitiveness, its mathematical–statistical representation and analysis is difficult due to the high number of dimensions of the factor space. Moreover, competitiveness cannot be measured directly only as a latent variable which is a feature of Principal Component Analysis (PCA). The introduced PCA, model by way of reducing the number of dimensions, can highlight the relations among the attributes and determine the most crucial issues capable of increasing rail competitiveness in the given countries and also of clustering those national railway markets. Recommendations for structural changes in national rail freight markets of the region are also supplied. Our results show that international rail competitiveness depends rather on market efficiency than on market liberalization due to the fact that the Global Competitiveness Index and Export/Import attributes did not significantly correlate with market concentration. As for the larger domestic rail freight sectors, small freight forwarders—spawned by liberalization—are shown to play a significant role in increasing competitiveness.

Urban Congestion Charging Acceptability: An International Comparative Study

“…Specifying the appropriate number of factors (K) that retains an adequate explanation of the data is not usually obvious. Therefore, principal component analysis (PCA) helps in setting this number [20]. Besides, the statistic called "Cronbach's Alpha" measures the internal consistency of amalgamation of several questions into one factor.…”

“…The authors also added that the income level of different groups in the society does not affect the acceptability of congestion charging significantly. However, if the scheme is implemented, the low-income group is more motivated to change its travel behavior to other transport modes like buses [20]. In contrast, high-income groups showed a tendency to keep using a passenger vehicle.…”

The Effect of China's Regional Economic Competitiveness on CO2 Emissions-Based on Economic Factors

“…The PCFA method (Principal component factor analysis) has been widely used for the evaluation of competitiveness (Duleba et al, 2019;Lu, 2019;Stanickova, 2015). The PCFA method has advantages in evaluating competitiveness.…”

Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

“…A patent infringement problem was executed with various classification methods using the standard SVM, principal component analysis (PCA) [44] + SVM, least square support vector machine (LSSVM), back propagation neural network (BPNN), least square support vector machine with new fuzzy selection (LSSVMFS), and the proposed SVMFS. The definition of patent infringement refers to the unlawful conduct of a patented invention without the permission of the patent holder.…”