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The growth of literature in the field of quality of service in the public transport (PT) sector shows increasing concern for a better understanding of the factors affecting service quality (SQ) in PT organizations and companies. A large variety of approaches to SQ has been developed in recent years owing to the complexity of the concept; the broad range of attributes required to evaluate SQ; and the imprecision, subjectivity and heterogeneous nature of the data used to analyse it. Most of these approaches are based on customer satisfaction surveys. This paper seeks to summarize the evolution of research and current thinking as it relates to the different methodological approaches for SQ evaluation in the PT sector over the years, and provides a discussion of future directions.

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The aim of this work is to analyze the different perceptions among groups of users regarding transit service quality. We propose a methodology based on a classification and regression tree (CART) approach, allowing the characteristics that most influence overall service quality to be identified. The methodology is applied by using data regarding rail service in northern Italy, particularly suburban lines connecting different towns of the hinterland of the city of Milan. Passengers expressed their opinions about service characteristics such as safety, cleanliness, comfort, information, and personnel. We found that perceptions about service quality are differentiated among the various groups of users.

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A transit service quality study based on cluster analysis was performed to extract detailed customer profiles sharing similar appraisals concerning the service. This approach made it possible to detect specific requirements and needs regarding the quality of service and to personalize the marketing strategy. Data from various customer satisfaction surveys conducted by the Transport Consortium of Granada (Spain) were analyzed to distinguish these groups; a decision tree methodology was used to identify the most important service quality attributes influencing passengers' overall evaluations. Cluster analysis identified four groups of passengers. Comparisons using decision trees among the overall sample of all users and the different groups of passengers identified by cluster analysis led to the discovery of differences in the key attributes encompassed by perceived quality.