Weighted Variables Using Best-Worst Scaling in Ordered Logit Models for Public Transit Satisfaction

Abstract

ABSTRACT: Customer overall satisfaction regarding a public transport system is dependent on the satisfaction of the users with the attributes that make up the service, as well as the contribution that each of these attributes makes to explain the overall satisfaction. A common way of analysing the contribution of service attributes to explain overall satisfaction is through the use of ordered logit or probit models. This article presents an ordered logit model that considers the weighting of independent variables through the explicit importance calculated on the basis of a best-worst case 1 choice task. For the calculation of importance, a multinomial logit model has been estimated which considers the heterogeneity of the sample through systematic variations in user tastes. In this way, it is possible to establish a level of importance of each specific attribute for each type of user. The results show that the importance varies considerably depending on different socio-economic and mobility-base variables. On the other hand, the inclusion of the weighted variables in the ordered logit model improves its fit. Therefore, the results make possible to develop policies focused on improving satisfaction on specific user targets