<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

Suan Dusit University Huahin Prachaup Khiri Khan

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The forecasting of future airline passenger demand is critical task for airline management. The objective of the present study was to develop an adaptive...

Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

Online veröffentlicht: 30. Apr. 2022

Seitenbereich: 151 - 159

DOI: https://doi.org/10.2478/ttj-2022-0013

Schlüsselwörter
adaptive neuro-fuzzy inference system (ANFIS), Australia, forecasting methods, domestic airlines, passenger forecasting

© 2022 Panarat Srisaeng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

Online veröffentlicht: 30. Apr. 2022

Seitenbereich: 151 - 159

DOI: https://doi.org/10.2478/ttj-2022-0013

Schlüsselwörteradaptive neuro-fuzzy inference system (ANFIS), Australia, forecasting methods, domestic airlines, passenger forecasting

© 2022 Panarat Srisaeng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
adaptive neuro-fuzzy inference system (ANFIS), Australia, forecasting methods, domestic airlines, passenger forecasting