# User Satisfaction Survey on Public Transport by a New PAHP Based Model
## 2 Literature Review  
Passenger satisfaction refers to the passengers' sense of either enjoyment or displeasure, which results from a comparison between function of the transport system and passenger's expectations; however, investigating passenger satisfaction and perceived comfort related to public transportation has been in focus of many researchers since the 1970s, and the evolution of the applied models and questionnaires is palpable [2]. One direction of this evolution is involving more and more specified attributes characterizing the public transport service quality. The Quattro project (1999) applied merely seven attributes plus a K coefficient for determining the equivalent travel time as a synthetic performance measure in urban public transport [3], while [4] used 25 manifest and 10 latent variables to evaluate passenger satisfaction. Specifically, for measuring satisfaction for public bus transport, [5] created the Service Quality Index (SQI), integrating 13 attributes such as bus travel time, transport fare, general cleanliness on board, or seat availability on bus, while [PERSON] et al. [6] applied 24 factors designed in a three-level-hierarchy. Obviously, avoiding the application of too many factors is highly recommended because many attributes make the evaluation process long and complicated; however, too few variables might not be sufficient for the appropriate analysis of passenger satisfaction. Based on empirical evidence [7; 8; 9; 10], the recommended number of attributes for characterizing passenger satisfaction for urban bus transport service is between 15 and 25. Since a significant reduction of the examined factors of public transport service quality decreases the chance for complex analysis, the other possibility for creating a suitable questionnaire and reaching research goals with limited number of attributes is to apply a methodology capable of revealing sufficient information from the survey data. The requirement for the selected technique is, on one hand, to make the evaluations as light as possible (for gathering representative number of responses) and, on the other hand, to provide the opportunity for proper analysis applying the given number of attributes. There has been a wide range of techniques applied for passenger satisfaction surveys. One of the earliest is the Stated Preference approach [11; 12], in which the participants evaluated individually the provided attributes on a three-option-scale: non-satisfactory, satisfactory and very satisfactory, and then, statistical methods were used for the analysis. As limitation, the restricted options for evaluation can be mentioned in these types of models, and other techniques strived to extend the scale of preference to gain better insight. [PERSON] and [PERSON] [13] applied also a statistical approach for evaluating transit service quality, combining subjective and objective measures. In the last decade, other complex statistical tools emerged for satisfaction surveys. [PERSON] and [PERSON] [14] applied Principal Component Analysis (PCA), which is another method for variable reduction and for examining inter-relations of the factors and their dependency to some shadow variables called principal components, to study the satisfaction of public transport services in Athens. [PERSON] and [PERSON] [15] investigated the relationship between passenger satisfaction and 20 service quality variables in railway transportation by PCA, while [PERSON] et al. [16] focused on the roles of service quality, perceived value, satisfaction and involvement of passengers of public transit by applying the same method. Two more multivariate statistical methods, the Discriminant Function Analysis (DFA) and Cluster Analysis (CA), have been also applied for obtaining the most influential factors in passenger satisfaction; [PERSON] and [PERSON] [17] modelled service quality in Granada, Spain, by Cluster Analysis in a decision tree model.