Travellers' Satisfaction with Railway Transport: A Bayesian Network Approach

Survey data are often employed as a tool to support planners in defining and reviewing their intervention programs and policies. In the current literature, a broad range of studies have examined the determinants and components of consumers' satisfaction. Most of these studies have exploited statistical models, such as probit or logit, to determine the categorical dependent variables. Less attention has been paid to the investigation of the structural properties of the data by testing alternative methods and procedures. The present work performs such an analysis using data from a survey on consumer satisfaction from railway transport in 14 EU countries. The data were collected in a large survey (more than 17,000 observations) conducted on behalf of the European Commission. By applying both ordered logistic regression and Bayesian Network (BN) analysis, the research question addressed by this paper is twofold. First of all, the performance of the two methodologies, defined in terms of their predictive capability, is assessed. Secondly, the main policy implications conveyed by the two models are compared. According to the results, BN analysis provides better predictions of the outcomes of customers' satisfaction with railway transport. Moreover, the choice of the statistical methodology is a relevant issue for policy-making, since the policy messages conveyed by the two models differ significantly.