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.
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The authors would like to express thanks to the Committee for Advanced Studies and Research (CASR) of Bangladesh University of Engineering and Technology (BUET) for the financial support.
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Comparison among author’s previous and current research
|Sl No.||Description||Islam et al. (2016a)||Islam et al. (2016b)||Current manuscript|
|1||Public Transport Modes||Bus||Bus||Train|
|3||Days||Regular Days||Regular Days||Both Regular and Special Days|
|4||Empirical Models||Several ANN (PNN, PRNN, and GRNN)||Specific ANN (PNN) which was best found on “Islam et al. (2016a)” and ANFIS||ANFIS only|
|5||SQ Attributes||Proximity from home, Proximity from workplace, Commuting frequency (daily), Service frequency, Commuting period (weekdays), Commuting period (weekends), Ticketing system, Fare expenditure (daily), Punctuality and reliability, Seat availability, Seat comfort, Accessibility to/from bus, Air ventilation system, On-board security, Female harassment, On-time performance, Bus staff courtesy, Structural condition, Interior cleanliness, Noise level, Commuting experience, Route information (total 22 Attributes)||Proximity from home, Proximity from workplace, Commuting frequency (daily), Service frequency, Commuting period (weekdays), Commuting period (weekends), Ticketing system, Fare expenditure (daily), Punctuality and reliability, Seat availability, Seat comfort, Accessibility to/from bus, Air ventilation system, On-board security, Female harassment, On-time performance, Bus staff courtesy, Structural condition, Interior cleanliness, Noise level, Commuting experience, Route information (total 22 Attributes)||Waiting place condition, Toilet cleanness, Fitness of car, Air Ventilation System, Convenience of online ticketing system, Seat comfort, Overall security, Travel delay, Ease at entry and exit, Courtesy of Employees, Travel cost, Female harassment, Convenience of ticket purchasing at counter, Noise insulation in car, Car arrangement, Meal service, Car cleanness, On-time performance (total 18 Attributes)|
|6||Tuning Membership Functions and Epochs during SQ Models Calibration||Not performed||Not performed||Rigorously performed|
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Hadiuzzaman, M., Malik, D.M.G., Barua, S. et al. Modeling passengers’ perceptions of intercity train service quality for regular and special days. Public Transp 11, 549–576 (2019). https://doi.org/10.1007/s12469-019-00213-0
- Intercity train
- Service quality
- Public opinion