1. bookVolume 23 (2022): Issue 2 (April 2022)
Journal Details
License
Format
Journal
eISSN
1407-6179
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English
access type Open Access

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

Published Online: 30 Apr 2022
Volume & Issue: Volume 23 (2022) - Issue 2 (April 2022)
Page range: 151 - 159
Journal Details
License
Format
Journal
eISSN
1407-6179
First Published
20 Mar 2000
Publication timeframe
4 times per year
Languages
English
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.

Keywords

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