Utilizing Advanced Modelling Approaches for Forecasting Air Travel Demand: A Case Study of Australia’s Domestic Low Cost Carriers A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Panarat Srisaeng MEcon. (Kasetsart University, Thailand) BEcon (Chulalongkorn University, Thailand) School of Aerospace Mechanical and Manufacturing Engineering College of Science Engineering and Health RMIT University March 2015 Declaration I certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis/project is the result of work which has been carried out since the official commencement date of the approved research program; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines have been followed. Panarat Srisaeng September 2015 Acknowledgement There are many people I wish to acknowledge for the support they have provided during my study at RMIT University. First, I would like to thank my supervisors, including Associate Professor Cees Bil, for the help and support given during the time of my candidature. In particular, I give special thanks to my co- supervisors, Dr Glenn Baxter and Dr Graham Wild, for their great help, support, guidance, dedication and assistance which enabled me to work through the critical phases of my study. Second, I would like to thank a number of individuals including: Dr Steve Richardson for his assistance and the valuable insights he gave me regarding the genetic algorithm modelling; Professor Heather Mitchell for her comments on Econometric modelling; Yousef Lutfi Abdel Latif Abu Nahleh and Subramanian Ramasamy who generously shared their knowledge and understandings with me about the ANFIS modelling. Third, my deep gratitude goes to Margaret Tein and Graham Matthews, who played multiple roles in my PhD journey, as second supervisor, supporters, problem solvers, editors, family members and more. I sincerely appreciate their professional guidance, warm support, generosity, love and the fact that they have always been my side to support me in times of hardship. Fourth, I would like to express my thanks to the Office of the Higher Education Commission (OHEC), Ministry of Education, Thailand and Suan Dusit University for providing the PhD scholarship. I also acknowledge Qantas Australia’s support, specifically in the person of Gareth Evans (CEO International), who provided me with considerable support for my research. As well, I want to say thank you: to my best friend in Thailand, who inspired me to start this PhD, for constant support, encouragement and companionship when I needed it the most; to Freda my best friend sister, for her great support and friendship; as well as Tingting my best buddy for her sincere help. As well I also wish to extend my special thanks to Caspar Cumming for his help and support during the journey. There were many other people and friends, who provided help and encouragement with my studies in RMIT, though your names may not be mentioned here, all of you will be always remembered in my heart. Finally, and most importantly, I would like to acknowledge and thank my family, my father, my sisters, and my uncle who have always supported, encouraged and blessed me in my academic aspirations. I dedicate this PhD thesis to my mother and my brother who are no longer with us, but are always in my thoughts, and I’m sure they are so proud of me. ABSTRACT One of the most pervasive trends in the global airline industry over the past few three decades has been the rapid development of low cost carriers. Australia has not been immune to this trend. Following deregulation of Australia’s domestic air travel market in the 1990s, a number of low cost carriers have entered the market, and these carriers have now captured around 31 per cent of the market. Australia’s low cost carriers require reliable and accurate passenger demand forecasts as part of their fleet, network, and commercial planning, product definition, and for scaling investments in fleet and their associated infrastructure. Historically, the multiple linear regression-based modelling approach has been the most popular and recommended method for forecasting airline passenger demand. In more recent times, however, new advanced artificial intelligence-based forecasting approaches – artificial neural networks (ANNs), genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) - have been applied in a broad range of disciplines. In light of the critical importance of passenger demand forecasts for airline management, as well as the recent developments in artificial intelligence-based forecasting methods, the key aim of this thesis was to specify and empirically examine three artificial intelligence-based approaches (ANNs, GA and ANFIS) as well as the multiple linear regression approach, in order to identify the optimum model for forecasting Australia’s domestic low cost carrier passenger demand. This is the first time that such models – enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model) – have been proposed and tested for forecasting Australia’s domestic low cost carrier passenger demand. The results show that of the four modeling approaches used in this study that the new, and novel, adaptive neuro-fuzzy inference system (ANFIS) approach provides the most accurate, reliable, and highest predictive capability for forecasting Australia’s domestic low cost carrier passenger demand. The accuracy of the forecasting models (PAX/RPKs) was measured by four goodness-of-fit measures: mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE). ANFIS PAX Model: MAE = 213.0, MAPE = 4.36 per cent, MSE = 7.1x104, and RMSE = 267.52. iii ANFIS RPKs Model: MAE = 218.97, MAPE = 5.55 per cent, MSE = 8.2x104, and RMSE = 286.81. A second aim of the thesis was to explore the principal determinants of Australia’s domestic low cost carrier passenger demand in order to achieve a greater understanding of the factors which influence air travel demand. In light of the evolving low cost carrier business models around the world, and the trend towards a ‘hybrid’ business model, a further aim of this thesis was to explore whether this strategy has also been adopted by Australia’s low cost carriers. The results show that the primary determinants of Australia’s domestic low cost carrier demand are Australia’s real best discount airfare, Australia’s population size, Australia’s real GDP, Australia’s real GDP per capita, Australia’s unemployment size, world jet fuel prices, Australia’s real interest rates, and tourism attractiveness. Interestingly three determinants, Australia’s unemployment size, tourism attractiveness, and real interest rates, which have not been empirically examined in any previously reported study of Australia’s domestic low cost carrier passenger demand, proved to be important predictor variables of Australia’s domestic low cost carrier passenger demand. The thesis also found that Australia’s low cost carriers have increasingly embraced a hybrid business model over the past decade. This strategy is similar to low cost carriers based in other parts of the world. The core outcome of this research, the fact that modelling based on artificial intelligence approaches is far more effective than the traditional linear models prescribed by the International Civil Aviation Organization (ICAO), means that future work is essential to validate this. From an academic perspective, the modelling presented in this study offers considerable promise for future air travel demand forecasting. The results of this thesis provide new insights into low cost carrier passenger demand forecasting methods and can assist low cost carrier executives, airports, aviation consultants, and government agencies with a variety of future planning considerations. iv TABLE OF CONTENTS ABSTRACT ........................................................................................................................... iii TABLE OF CONTENTS ........................................................................................................ v LIST OF PUBLICATIONS ................................................................................................... xiv CHAPTER ONE: INTRODUCTION ....................................................................................... 1 1.1 Background ................................................................................................................. 1 1.2 The Research Gap ...................................................................................................... 3 1.3 Research Aims and Research Questions .................................................................... 5 1.4 Significance and Impact of the Study ........................................................................... 8 1.5 Outline of the Thesis .................................................................................................. 10 CHAPTER TWO: ESTABLISHING THE CONTEXT: AUSTRALIA’S EVOLVING LOW COST CARRIER MARKET ............................................................................................................ 11 2.1 Introduction ................................................................................................................ 11 2.2 Evolution