An Integrated Ordered Probit Model for Evaluating University Commuters’ Satisfaction with Public Transport

Abstract

Transport policymakers need to have an in-depth understanding of public transport (PT) customers in order to effectively manage transport systems and maintain the attractiveness of these systems to potential users. This research aims to compare the perceptions and satisfaction levels of two groups of PT users (habitual and occasional) among university staff and students regarding the quality of PT through a new integrated approach. A sample of 500 participants from Budapest, Hungary was used. Two stages of analysis were conducted: a descriptive analysis was conducted in the first stage, and Student’s t-tests of two independent samples were applied to identify the varying perceptions and overall satisfaction. Second, a new integrated ordered probit model (OPM) and an importance–performance analysis (IPA) were used to envisage how best to prioritize actions for transport enhancement. The results show that in the circle of commuters, the habitual PT users were more satisfied with the existing PT service than the occasional PT users. According to the findings of the IPA, for habitual users, the attribute “information provided” has a high priority for improvement, whereas the cost for both user types was found to be significant for all models, contributing to overall satisfaction. This factor was included in the possible overkill quadrant, suggesting that there might be more cost resources than needed. The new model, along with the case study results, may help policymakers and transport operators to make better decisions regarding the identification of service priority areas.

Towards Intercity Mobility System – Insights into the Spatial Interaction Gravity Model and Determination Approach

“…Concerning the VIF threshold, researchers have extensively suggested a value of 10 [29, 30], which corresponds to the tolerance suggestion of 0.1. Drawn from the findings by [31][32][33], the VIF less than 10 (i.e. the tolerance greater than 0.1) for each independent variable indicates that there is no multicollinearity issue between variables.…”