Utilizing advanced modelling approaches for forecasting air travel demand: a case study of Australia’s domestic low cost carriers

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 (LCCs). Australia has not been immune to this trend. Following deregulation of Australia’s domestic air travel market in the 1990s, a number of LCCs have entered the market, and these carriers have now captured around 31 per cent of the market. Australia’s LCCs require reliable and accurate passenger demand forecasts as part of their fleet, network, and commercial planning and for scaling investments in fleet and their associated infrastructure. Historically, the multiple linear regression (MLR) 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 MLR approach, in order to identify the optimum model for forecasting Australia’s domestic LCCs demand. This is the first time that such models – enplaned passengers (PAX) and revenue passenger kilometres performed (RPKs) – have been proposed and tested for forecasting Australia’s domestic LCCs demand. The results show that of the four modeling approaches used in this study that the new, and novel, ANFIS approach provides the most accurate, reliable, and highest predictive capability for forecasting Australia’s LCCs demand. A second aim of the thesis was to explore the principal determinants of Australia’s domestic LCCs demand in order to achieve a greater understanding of the factors which influence air travel demand. The results show that the primary determinants of Australia’s domestic LCCs demand are real best discount airfare, population, real GDP, real GDP per capita, unemployment, world jet fuel prices, real interest rates, and tourism attractiveness. Interestingly three determinants, unemployment, tourism attractiveness, and real interest rates, which have not been empirically examined in any previously reported study of Australia’s domestic LCCs demand, proved to be important predictor variables of Australia’s domestic LCCs demand. The thesis also found that Australia’s LCCs have increasingly embraced a hybrid business model over the past decade. This strategy is similar to LCCs 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 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 LCCs passenger demand forecasting methods and can assist LCCs executives, airports, aviation consultants, and government agencies with a variety of future planning considerations

Forecasting demand for low cost carriers in Australia using an artificial neural network approach

This study focuses on predicting Australia's low cost carrier passenger demand and revenue passenger kilometres performed (RPKs) using traditional econometric and artificial neural network (ANN) modelling methods. For model development, Australia's real GDP, real GDP per capita, air fares, Australia's population and unemployment, tourism (bed spaces) and 4 dummy variables, utilizing quarterly data obtained between 2002 and 2012, were selected as model parameters. The neural network used multi-layer perceptron (MLP) architecture that compromised a multi- layer feed-forward network and the sigmoid and linear functions were used as activation functions with the feed forward‐back propagation algorithm. The ANN was applied during training, testing and validation and had 11 inputs, 9 neurons in the hidden layers and 1 neuron in the output layer. When comparing the predictive accuracy of the two techniques, the ANNs provided the best prediction and showed that the performance of the ANN model was better than that of the traditional multiple linear regression (MLR) approach. The highest R-value for the enplaned passengers ANN was around 0.996 and for the RPKs ANN was round 0.998, respectively

The responses of traditional carriers to low-cost carriers

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Using an artificial neural network approach to forecast Australia's domestic passenger air travel demand

The aim of this work is to utilise an artificial neural network (ANN) to model Australia"s domestic air travel demand. This modelling will then facilitate forecasting future passenger demand. Forecasting passenger demand is a critical issue in the air transport industry and is generally viewed as the most crucial function of airline management This is the first time an ANN has been applied to domestic air travel in Australia, with ANN approaches having limited use in the industry. Two ANN models to forecast Australia's domestic airline passenger demand (PAX model) and revenue passenger kilometres performed (RPKs model) were constructed. Quarterly data from 1992 to 2014 was used. Australia's real interest rates and tourism attractiveness were included as candidate variables for the fast time in the models. As with the conventional ICAO approach to forecasting, GDP and airfare were significant factors, along with unemployment, jet fuel, and accommodation beds due to the large portion of the market to tourism

Demand for low-cost airlines in Australia

The purpose of this study was analysis of low-cost airline demand in Australia. As part of this project, an econometric method was applied to develop a regression model for forecasting demand. The research hypothesis being that low-cost airline demand in Australia is based on the following variables: domestic airfares, price of other transport modes,population, disposable income and tourist numbers. It was found that demand for low-cost airlines is primarily a function of domestic airfare and population while tourist numbers and price of other transport modes did not have a significant influence

The evolution of low cost carriers in Australia

Due to the vast distances across the country as well as between urban centres, Australia is heavily reliant upon its air transport industry. Following deregulation of Australia's domestic air travel market on the 30th October, 1990, low cost carriers have entered the market. Australia's LCC market has had three discrete phases. The first wave occurred between 1990 and 1993 and was subsequently followed by a duopoly period in 1994-1999. The second wave occurred between 2000 and 2006 and the final wave has been in the post-2006 period. This paper examines the evolution of Australia's domestic low cost carrier airline market and finds that by 2010, low cost carriers had captured around 64 per cent of the market. Following the evolution of the "Virgin Australia" business model from a low cost carrier to a full service network carrier, commencing in 2011, the low cost carrier's market share has declined significantly and is now around 31 per cent. "Jetstar" and "Tiger Airways" are the two major carriers presently operating in this market segment

An adaptive neuro-fuzzy inference system for forecasting Australia's domestic low cost carrier passenger demand

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia's domestic low cost carriers' demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the 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. Data was normalized in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities

An adaptive neuro-fuzzy inference system for modelling Australia's regional airline passenger demand

This study has proposed and empirically tested for the first time two adaptive neuro-fuzzy inference system (ANFIS) models for forecasting Australia's regional airline passenger demand, as measured by enplaned passengers (PAX model) and revenue passenger kilometres performed (RPKs model). In ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. 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. Data was normalised in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the out of sample testing dataset of the PAX and RPKs models was 5.40% and 6.91%, respectively

An Assessment Of Airport Sustainability, Part 2-Energy Management At Copenhagen Airport

Airports play a critical role in the air transport value chain. Each air transport value chain stakeholder requires energy to conduct their operations. Airports are extremely energy intensive. Greenhouse gases are a by-product from energy generation and usage. Consequently, airports are increasingly trying to sustainably manage their energy requirements as part of their environmental policies and strategies. This study used an exploratory qualitative and quantitative case study research approach to empirically examine Copenhagen Airport, Scandinavia's major air traffic hub, sustainable airport energy management practices and energy-saving initiatives. For Copenhagen Airport, the most significant environmental impact factors occurring from energy usage are the CO2 emissions arising from both the air side and land side operations. Considering this, the airport has identified many ways to manage and mitigate the environmental impact from energy consumption on both the air and land side operations. Importantly, the application of technological solutions, systems and process enhancements and collaboration with key stakeholders has contributed to the airport's success in mitigating the environmental impact from energy usage at the airport whilst at the same time achieving energy savings

An assessment of airport sustainability, Part 1-waste management at Copenhagen Airport

Airports play a vital role in the air transport industry value chain, acting as the interface point between the air and surface transport modes. However, substantial volumes of waste are produced as a by-product of the actors' operations. Waste management is therefore becoming especially important to airports. Using a qualitative and quantitative case study research approach, this paper has examined the waste management strategies and systems at Copenhagen Airport, Scandinavia's major air traffic hub, from 1999 to 2016. The two major sources of waste at Copenhagen Airport are the waste generated from aircraft serving the airport and the waste arising from ground activities undertaken in the land and airside precincts. The growth in passengers and aircraft movements has had a concomitant impact on the volume of waste generated. Swept waste and sludge are processed by an external provider. Waste generated in the passenger terminals and the airport operator's facilities is handled at a central container station, where it is sorted for incineration, recycling or for landfill. The environmental impact of the waste produced at the airport is mitigated through the recycling of waste wherever possible. © 2018 by the authors