Abstract. 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 R 2 -value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

Airports are an essential infrastructure to facilitate aviation. The substantial growth of aviation has led to a significant increase in water usage by airports. Airports also generate large volumes of wastewater that may include contaminants. Hence, understanding sustainable water management practices is essential in the aviation industry. In this study, an exploratory research design was utilized in the examination of the sustainable water management strategies and systems at Kansai International Airport from 2002 to 2016. The qualitative data were examined using document analysis as part of a case study. The quantitative data were analyzed using regression analysis as part of a longitudinal study. The airport has been able to reduce the total water consumption, water consumption per passenger, and water consumption per aircraft movement, even with increased traffic in recent years. The airport sources water from the municipal authorities and reclaims water for non-potable water uses. The airport conducts regular water quality tests which measure the Chemical oxygen demand, total nitrogen, and total phosphates. The airport’s onsite wastewater processing centre processes all wastewaters, which discharges non-reclaimed water into Osaka Bay. With a decrease in water consumption, there has similarly been a decrease in the need to treat wastewater, while the reclaimed water ratio has increased over the period of the study.

This paper presents a case study of the DHL Express and Lufthansa Cargo strategic joint venture cargo airline 'AeroLogic', the global air cargo industry's largest operative joint venture between an airline and a leading international express and logistics provider. The study used a qualitative research approach. The data gathered for the study was examined by document analysis. The strategic analysis of the AeroLogic joint venture was based on the use of Porter's Five Forces framework. The study found that the AeroLogic joint venture airline has provided synergistic benefits to both partners and has allowed the partners to access new markets and to participate in the evolution of the air cargo industry. The new venture has also enabled both joint venture partners to enhance their competitive position in the global air cargo industry through strengthened service offerings and has provided the partners with increased cargo capacities, a larger route network, and greater frequencies within their own route networks. The study also found that the AeroLogic business model is unique in the air cargo industry.

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.

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 CO 2 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.

Abstract:The global air transport industry is predicted to continue its rapid growth. A by-product of air transport operations, however, is the substantial volumes of waste generated at airports. To mitigate the environmental impact of waste and to comply with regulatory requirements, airports are increasingly implementing sustainable waste management policies and systems. Using an in-depth case study research design, this study has examined waste management at Kansai International Airport from 2002 to 2015. Throughout its history the airport has implemented world best practices to achieve its goal of being an eco-friendly airport. The qualitative data gathered for the study were analysed using document analysis. The quantitative data were analysed using t-tests. Statistically significant results were found in the reduction in waste per passenger and aircraft movement (for total waste, incinerated waste, and landfill waste). In addition, a statistically significant increase in the proportion of waste recycled, and a decrease in the proportion of waste sent to landfill was observed. As such, quantitatively speaking, Kansai International Airport has shown significant waste management improvements. The study concludes that Kansai Airport's waste management approaches and policies can be transferred to other airport facilities. This would greatly improve sustainability across airports, globally.

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.

No abstract