Risk Assessment under Uncertainty

System safety assessment (SSA) has become a standard practice in air traffic management (ATM). System safety assessment aims, through a systematic and formal process, to detect, quantify, and diminish the derived risks and to guarantee that critical safety systems achieve the level of safety approved by the regulatory authorities. Verification of compliance with the established safety levels becomes the last but an essential part of the safety assurance process. This chapter provides a Bayesian inference methodology to assess and evaluate the compliance with the established safety levels under the presence of uncertainty in the assessment of systems performances

Designing and Developing New VET Curricula to Address Skills Gaps in the Aeronautics Industry

Aeronautic industry is one of the enablers of the economic development and social insertion at European level, and it is one of the drivers of expansion of remote regions as far as it allows habitants, workers, and companies at these regions to expand their activities and areas of influence. The European aeronautics industry, including the commercial air transport, generates more than 220 billion of Euros and more than 4.5 million of jobs. These figures are expected to be double by 2030. Future developments in the sector, together with greater intra-European mobility of workers and population aging, bring a greater need for new skills in the work force together with an urgency for a larger number of professionals. Therefore, to achieve the desirable sustained growth the EU needs to invest in high-quality VET (vocational education and training) in order to be able to supply the AI (aeronautic industry) with qualified workers. VET stands for education and training which aims to equip people with knowledge, know-how, skills and/or competences required in particular occupations or more broadly on the labor market. This work discusses the outcomes of the AIRVET project, an European partnership whose principal objective is the development and promotion of “curricula and VET courses” in the Maintenance and Information and Communications Technologies (ICT) domains, required for a highly skilled aeronautical workforce

Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation

Most decisions in aviation regarding systems and operation are currently taken under uncertainty, relaying in limited measurable information, and with little assistance of formal methods and tools to help decision makers to cope with all those uncertainties. This chapter illustrates how Bayesian analysis can constitute a systematic approach for dealing with uncertainties in aviation and air transport. The chapter addresses the three main ways in which Bayesian networks are currently employed for scientific or regulatory decision-making purposes in the aviation industry, depending on the extent to which decision makers rely totally or partially on formal methods. These three alternatives are illustrated with three aviation case studies that reflect research work carried out by the authors

How Much Can Carbon Taxes Contribute to Aviation Decarbonization by 2050

Aviation emissions from 2016 to 2050 could consume between 12% and 27% of the remaining carbon budget to keep global temperature rise below 1.5 °C above preindustrial levels. Consequently, aviation is being challenged to immediately start to reduce its in-sector emissions, then sharply reduce its CO2 emissions and fully decarbonize toward the second half of this century. Among the analyses carried out within the Horizon 2020 project PARE—Perspectives for Aeronautical Research in Europe, this paper tackles the potential role of climate change levy schemes in achieving the ambitious objective of aviation decarbonization by the year 2050

Bayesian Network Modelling of ATC Complexity Metrics for Future SESAR Demand and Capacity Balance Solutions

Demand & Capacity Management solutions are key SESAR (Single European Sky ATM Research) research projects to adapt future airspace to the expected high air traffic growth in a Trajectory Based Operations (TBO) environment. These solutions rely on processes, methods and metrics regarding the complexity assessment of traffic flows. However, current complexity methodologies and metrics do not properly take into account the impact of trajectories’ uncertainty to the quality of complexity predictions of air traffic demand. This paper proposes the development of several Bayesian network (BN) models to identify the impacts of TBO uncertainties to the quality of the predictions of complexity of air traffic demand for two particular Demand Capacity Balance (DCB) solutions developed by SESAR 2020, i.e., Dynamic Airspace Configuration (DAC) and Flight Centric Air Traffic Control (FCA). In total, seven BN models are elicited covering each concept at different time horizons. The models allow evaluating the influence of the “complexity generators„ in the “complexity metrics„. Moreover, when the required level for the uncertainty of complexity is set, the networks allow identifying by how much uncertainty of the input variables should improve