Browsing by Author "Steven Richardson"

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    A forecasting tool for predicting Australia�s domestic airline passenger demand using a genetic algorithm
    (Departamento de Ciencia e Tecnologia Aeroespacial, 2015) Panarat Srisaeng; Glenn Baxter; Steven Richardson; Graham Wild; G. Wild; RMIT University, School of Aerospace, Mechanical, and Manufacturing Engineering, Melbourne, 124 La Trobe Stm, 3000, Australia; email: graham.wild@rmit.edu.au
    This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia�s domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively. � 2015, Journal of Aerospace Technology and Management. All Rights Reserved.
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    Machine Learning for Air Transport Planning and Management
    (American Institute of Aeronautics and Astronautics Inc, AIAA, 2022) Graham Wild; Glenn Baxter; Panarat Srisaeng; Steven Richardson
    In this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the economic and financial aspects of the industry. The traditional approach used in airline operations as specified by the International Civil Aviation Organization is the use of a multiple linear regression (MLR) model, utilizing cost variables and economic factors. Here, the performance of models utilizing an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support vector machine, and a regression tree are compared to MLR. The ANN and ANFIS had the best performance in terms of the lowest mean squared error. © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
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    Machine Learning for Air Transport Planning and Management
    (American Institute of Aeronautics and Astronautics Inc, AIAA, 2022) Graham Wild; Glenn Baxter; Pannarat Srisaeng; Steven Richardson
    In this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the economic and financial aspects of the industry. The traditional approach used in airline operations as specified by the International Civil Aviation Organization is the use of a multiple linear regression (MLR) model, utilizing cost variables and economic factors. Here, the performance of models utilizing an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support vector machine, and a regression tree are compared to MLR. The ANN and ANFIS had the best performance in terms of the lowest mean squared error. © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.