Collecting data to obtain insights into customer satisfaction with public transport services is very time-consuming and costly. Many factors such as service frequency, reliability and comfort during the trip have been found important drivers of customer satisfaction. Consequently, customer satisfaction surveys are quite lengthy, resulting in many interviews not being completed within the aboard time of the passengers/respondents. This paper questions as to whether it is possible to reduce the amount of information collected without a compromise on insights. To address this research question, we conduct a comparative analysis of different Ordered Probit models: one with a full list of attributes versus one with partial set of attributes. For the latter, missing information was imputed using three different methods that are based on modes, single imputations using predictive models and multiple imputation. Estimation results show that the partial model using the multiple imputation method behaves in a similar way to the model that is based on the full survey. This finding opens an opportunity to reduce interview time which is critical for most customer satisfaction surveys.

The strengthening of the railway mode is one of the main policies that can help to achieve more sustainable future mobility. A methodology is presented to estimate future transport demand scenarios over the medium (2030) and long term (2050) considering three trip lengths: urban, regional, and long distance for two trip purposes: work and leisure. The objective of the scenarios is to answer the question: would railways benefit from a 10% drop in car usage? Qualitative techniques have been applied, based on in-depth interviews and focus groups, as well as a stated preference survey where the people sampled answered to different scenarios which included the presence of a new train, with a high level of service, and other possible new modes of transport such as autonomous cars and buses. The results obtained using Best-Worst discrete choice models show that the rail mode could capture a significant part of the demand from the car over all three journey lengths, but especially for long distance, if the new rail mode were introduced.

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.