Multivariate techniques for analyzing the perceived quality of public bus service in Costa Rica (Revista Científica Compendium, 2025)
In increasingly congested and polluted cities, it is of utmost importance to retain public transport users and seek to increase its use, so as to prevent an increase in travel times, emissions and other consequences derived from intensive use of private cars. One of the key aspects for achieving this is to manage the quality of public transport service. However, measuring service quality can be multifaceted and complex, requiring different indicators to evaluate the service provided. This can make managing an already complex concept even more difficult. This study demonstrates how Decision Tree Algorithm and logistic and multinomial regressions can be used to determine variables that may affect the quality of public transportation services. In addition, this study shows improvements in aspects related to safety, travel times and bus stops, which can lead to progress in the quality of public transport services.
Keywords: logistic regression; multinomial regression; transit quality of service; Decision Tree Algorithm