# An Integrated Decision Support Model for Evaluating Public Transport Quality
## 1 Introduction
Reference [11] suggested a combination of Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Pattern Recognition Neural Network (PRNN) approaches to analyze the service quality of public transport in Dhaka, Bangladesh. In their study, the physical comfort, reliability, and time availability were the most significant criteria from users' perspective. Reference [12] examined public satisfaction and perceptions towards public transport quality in Granada. As a methodology, they conducted nonlinear principal component analysis (NLPCA) with a logit multilevel model (LMLM). The study conducted by [13] evaluated the service quality criteria of public bus transport in Batna, Algeria, which included the satisfaction degree toward the system by measuring and applying a survey based on factor analysis. More recently, there is growth in the use of decision support methodologies, not only to evaluate and develop public transport supply quality but also to increase passengers' satisfaction level. Reference [14] aimed to explore the most important factors affecting public bus transport supply quality within Yurihonjo, Japan. They adopted the Analytic Hierarchy Process (AHP) approach for prioritizing the supply quality criteria, and three different main groups (users, government, and the transport operator company) participated in the questionnaire survey; as a result, approachability was the most significant issue for users and government, while it was the third most significant issue for operators. Reference [15] adopted a novel combination of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) to evaluate the service quality of the Taiwanese airline industry. Reference [16] proposed a novel Multi Criteria Decision Making (MCDM) approach based on Group Analytic Hierarchy Process (GAHP) and Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) with the Delphi method to improve Tehran's public transport and estimate users' satisfaction for supporting decision makers in their decisions. The results illustrated the metro as the most important mode and the local government representatives must pay attention to this issue. Reference [17] integrated MCDM methods with fuzzy sets to improve the quality of airline service in Turkey; the results obtained expressed evaluators' preferences are more reliable and suitable due to utilizing fuzzy sets, which provide more accurate decisions. Reference [18] utilized combined AHP and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to estimate public bus transport service quality; the results illustrated the convenience and frequency as the most significant service quality criteria from the passengers' point of view. Reference [19] created a new aggregation method by adding an extra level of stakeholder groups (users, nonusers, and local government); moreover, Kendall rank correlation was used to detect the gap between the groups' preferences, where the results showed a significant gap between users' and the local government's opinion in Mersin, Turkey. Reference [20] presented a novel approach of AHP with Pareto optimality to develop the quality service of public transport. This model led to better approximation for evaluators--judgments that reflected the real intention of the decision maker. Reference [21] detected passengers' demand toward developing public transport supply quality in Amman, Jordan, by conducting the conventional AHP and extended the hierarchy structure of public bus transport supply quality of [14] by injecting the fare as a main criterion in the system; the outcomes showed the fare as the third most important criteria from passengers' point of view. To deal with the inconsistencies and uncertainties of citizens' answers in evaluating the urban transport quality, [22] adopted the interval AHP approach and compared users' perspectives with nonusers' and experts' point of view.