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Modeling passengers’ perceptions of intercity train service quality for regular and special days

Modeling passengers’ perceptions of intercity train service quality for regular and special days The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in this study to model the non-linear relationship between intercity train service quality (SQ) and its attributes related to physical conditions and service features. We use Likert scale questionnaire survey data from 1037 and 553 users to calibrate the ANFIS structures for intercity train SQ estimation for regular days and special days, respectively. The influences of membership functions (MFs) and epochs on ANFIS performance are assessed to capture heterogeneity in the collected SQ data. Based on this study, it is found that the effect of epochs is insignificant for a higher number of epochs. Moreover, the Gaussian-type MF incorporated into the ANFIS structure fits the collected survey data better than other distributions. Overall, the proposed ANFIS structures with 18 attributes show 54.1% and 60.2% accuracy in predicting train SQ for regular days and special days, respectively. A stepwise approach is followed for ranking the intercity train SQ attributes influencing its overall SQ and the results are compared with those of the empirical observations (public opinions). The study implies that besides waiting place condition, attributes related to physical conditions and service features of intercity train are important determinants of its perceived SQ for regular days and special days, respectively. These results help in identifying the characteristics that are important to SQ perception. This can help transit planners and managers in targeting improvement investments that will be most effective to help commuters think more positively about their trips. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Public Transport Springer Journals

Modeling passengers’ perceptions of intercity train service quality for regular and special days

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References (47)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Business and Management; Operations Research/Decision Theory; Automotive Engineering; Computer-Aided Engineering (CAD, CAE) and Design; Transportation
ISSN
1866-749X
eISSN
1613-7159
DOI
10.1007/s12469-019-00213-0
Publisher site
See Article on Publisher Site

Abstract

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in this study to model the non-linear relationship between intercity train service quality (SQ) and its attributes related to physical conditions and service features. We use Likert scale questionnaire survey data from 1037 and 553 users to calibrate the ANFIS structures for intercity train SQ estimation for regular days and special days, respectively. The influences of membership functions (MFs) and epochs on ANFIS performance are assessed to capture heterogeneity in the collected SQ data. Based on this study, it is found that the effect of epochs is insignificant for a higher number of epochs. Moreover, the Gaussian-type MF incorporated into the ANFIS structure fits the collected survey data better than other distributions. Overall, the proposed ANFIS structures with 18 attributes show 54.1% and 60.2% accuracy in predicting train SQ for regular days and special days, respectively. A stepwise approach is followed for ranking the intercity train SQ attributes influencing its overall SQ and the results are compared with those of the empirical observations (public opinions). The study implies that besides waiting place condition, attributes related to physical conditions and service features of intercity train are important determinants of its perceived SQ for regular days and special days, respectively. These results help in identifying the characteristics that are important to SQ perception. This can help transit planners and managers in targeting improvement investments that will be most effective to help commuters think more positively about their trips.

Journal

Public Transport – Springer Journals

Published: Nov 25, 2019

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