A recent alleged "drone" collision with a British Airways Airbus A320 at Heathrow Airport highlighted the need to understand civil Remotely Piloted Aircraft Systems (RPAS) accidents and incidents (events). This understanding will facilitate improvements in safety by ensuring efforts are focused to reduce the greatest risks. One hundred and fifty two RPAS events were analyzed. The data was collected from a 10-year period (2006 to 2015). Results show that, in contrast to commercial air transportation (CAT), RPAS events have a significantly different distribution when categorized by occurrence type, phase of flight, and safety issue. Specifically, it was found that RPAS operations are more likely to experience (1) loss of control in-flight, (2) events during takeoff and in cruise, and (3) equipment problems. It was shown that technology issues, not human factors, are the key contributor in RPAS events. This is a significant finding, as it is contrary to the industry view which has held for the past quarter of a century that human factors are the key contributor (which is still the case for CAT). Regulators should therefore look at technologies and not focus solely on operators.

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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.

abstract. A number of full service network carriers have recently stated their ambition to develop certain ultra-longrange (ULR) routes, such as Doha to Auckland, Dubai to Auckland, Dubai to Panama City, Singapore to San Francisco, Singapore to New York, all of which require a great circle distance between 7,000-9,000 nautical miles (nm) with an estimated travel time between 15 and 20 hours. This paper examines the capability of the current generation of wide-bodied passenger aircraft to satisfy this evolving strategy, and the impact, if any, on the provision of air cargo transportation. An exploratory study is presented herein based on an assessment of each aircraft type's payload-range envelope, taken from the appropriate Aircraft Airports Handling Characteristics Manual. The key findings reveal that airlines wishing to pursue this ultra-longrange strategy have a surprisingly limited choice of current-generation passenger aircraft which are capable of flying the desired mission profile without compromising significantly on passenger numbers and cargo payload.

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.