Predicting Australia's Domestic Airline Passenger Demand using an Anfis Approach
| dc.contributor.author | Panarat Srisaeng | |
| dc.contributor.author | Glenn Baxter | |
| dc.date.accessioned | 2025-03-10T07:35:06Z | |
| dc.date.available | 2025-03-10T07:35:06Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The forecasting of future airline passenger demand is critical task for airline management. The objective of the present study was to develop an adaptive neuro-fuzzy inference system (ANFIS) for predicting Australia's domestic airline passenger demand. The ANFIS model was trained, tested, and validated in the study. Sugeno fuzzy rules were used in the ANFIS structure and Gaussian membership function, and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The results found that the mean absolute percentage error (MAPE) for the overall data set of the ANFIS model was 3.25% demonstrating that the ANFIS model has high predictive capabilities. The ANFIS model could be used in other domestic air travel markets. © 2022 Panarat Srisaeng et al., published by Sciendo. | |
| dc.identifier.citation | Transport and Telecommunication | |
| dc.identifier.doi | 10.2478/ttj-2022-0013 | |
| dc.identifier.issn | 14076160 | |
| dc.identifier.scopus | 2-s2.0-85129805713 | |
| dc.identifier.uri | https://repository.dusit.ac.th//handle/123456789/4631 | |
| dc.language | English | |
| dc.publisher | Sciendo | |
| dc.rights | All Open Access; Gold Open Access | |
| dc.rights.holder | Scopus | |
| dc.subject | adaptive neuro-fuzzy inference system (ANFIS) | |
| dc.subject | Australia | |
| dc.subject | domestic airlines | |
| dc.subject | forecasting methods | |
| dc.subject | passenger forecasting | |
| dc.title | Predicting Australia's Domestic Airline Passenger Demand using an Anfis Approach | |
| dc.type | Article | |
| mods.location.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129805713&doi=10.2478%2fttj-2022-0013&partnerID=40&md5=ab365e81dfaa0bbb7a9d46084a7d7c6c | |
| oaire.citation.endPage | 159 | |
| oaire.citation.issue | 2 | |
| oaire.citation.startPage | 151 | |
| oaire.citation.volume | 23 |