# User Satisfaction Survey on Public Transport by a New PAHP Based Model
## 2 Literature Review
Even though these models were capable of statistically correct analysis, the complex interdependencies of the attributes needed an improved approach for drawing appropriate conclusions. A large group of techniques for exploring the correlation between passenger satisfaction and service quality elements is the family of Structural Equation Modelling (SEM) methods. A good example of creating a SEM model for passenger perception analysis is the paper of [PERSON] and [PERSON] [18], in which the authors identified 33 different service quality attributes, e.g., crowding on board, frequency of runs or information at stations. Passengers evaluated the importance and the satisfaction of each attribute on a 1 to 10 scale, and their rate could be calculated. The next step of the SEM procedure was to create latent exogenous variables which were basically more general items of the service quality: Safety, Cleanliness, Comfort, Service, Additional services, Information and Personnel. An endogenous latent variable was also introduced: the Service quality itself. Finally, solving the structural equations of the variables and latent variables produced the regression weights for the impact analysis of each attribute, which led to concluding the importance of each service quality item in passenger satisfaction. This research demonstrates that by the application of a structural equation model, the strength of correlations among transport service quality attributes can be quantified and measured for both direct and indirect effects [19]. Further, SEM has the clear benefit of determining latent attributes or aspects in transport service quality measures [20]. Moreover, SEM models are capable of considering not only positive but also negative impacts of the attributes on each other or on the exogenous or endogenous latent variables. However, a significant drawback of these type of techniques is that the consistency of responses is not measured, and in the case of layman evaluators as passengers, the users of public transportation, the risk of getting inconsistent responses in a survey is relatively high. Despite this drawback, the popularity of SEM models in transport research is still very high [21; 22; 23; 24; 25; 26; 27]. Other modelling techniques have also made significant impact in service quality analysis: Ordered Data Models [28; 29], decision tree [30] benchmarking concept [31] or Bayesian methods [32] to list some of the most significant ones. Providing a broader preference scale for better expressing public satisfaction, [PERSON] et al. [6] applied Analytic Hierarchy Process (AHP) for supporting the public bus transport system development decision in a Japanese city, Yurihonjo. It has to be emphasized that for every MCDM survey, especially for AHP, the efforts of the evaluators are higher [33] not only because of the higher number of questions but also due to the type of questions to be answered. Pairwise comparisons require more cognitive effort from the participants compared to other, direct evaluations, e.g., using a Likert-scale [34]. There have been more recent AHP based models applied for measuring public transport passenger satisfaction; [PERSON] and [PERSON] [35] created an AHP-Kendall model for determining the distance of preferences between public and expert participants in a public transportation system. [PERSON] et al. [36] applied an Interval-AHP model for obtaining more accurate results from passenger evaluators on public service quality, and [PERSON] et al. [37] compared Fuzzy AHP and Interval AHP final scores for getting more trustworthy insights into passenger satisfaction with the public bus transport system of a Turkish big city, Mersin. As literature review synthesis, it can be stated that SEM models have the advantages of conducting relatively simple and fast surveys and also of considering positive and negative interdependencies of the service quality attributes, while MCDM techniques can produce more reliable analysis outcomes due to the consistency measure, which is an asset in passenger surveys.