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

Discussion on density-based clustering methods applied for automated identification of airspace flows

Air Traffic Management systems generate a huge amount of track data daily. Flight trajectories can be clustered to extract main air traffic flows by means of unsupervised machine learning techniques. A well-known methodology for unsupervised extraction of air traffic flows conducts a two-step process. The first step reduces the dimensionality of the track data, whereas the second step clusters the data based on a density-based algorithm, DBSCAN. This paper explores advancements in density-based clustering such as OPTICS or HDBSCAN*. This assessment is based on quantitative and qualitative evaluations of the clustering solutions offered by these algorithms. In addition, the paper proposes a hierarchical clustering algorithm for handling noise in this methodology. This algorithm is based on a recursive application of DBSCAN* (RDBSCAN*). The paper demonstrates the sensitivity of these algorithms to different hyper-parameters, recommending a specific setting for the main one, which is common for all methods. RDBSCAN* outperforms the other algorithms in terms of the density-based internal validity metric. Finally, the outcome of the clustering shows that the algorithm extracts main clusters of the dataset effectively, connecting outliers to these main clusters

A case study of fishbone sequential diagram application and ADREP taxonomy codification in conventional ATM incident investigation

This paper aims to present the application of a fishbone sequential diagram in air traffic management (ATM) incident investigations performing as a key connection between safety occurrence analysis methodology (SOAM) and accident/incident data reporting (ADREP) approaches. SOAM analysis is focused on organizational cause detection; nevertheless, this detection of individual causes from a complete incident scenario presents a complex analysis, and even more, the chronological relationship between causes, which is lacking in SOAM, should be tracked for post-investigation analysis. The conventional fishbone diagram is useful for failure cause classification; however, we consider that this technique can also show its potential to establish temporal dependencies between causes, which are categorized and registered with ADREP taxonomy for future database creation. A loss of separation incident that occurred in the Edmonton area (Canada) is used as a case study to illustrate this methodology as well as the whole analysis process

A machine learning approach to air traffic interdependency modelling and its application to trajectory prediction

Air Traffic Management is evolving towards a Trajectory-Based Operations paradigm. Trajectory prediction will hold a key role supporting its deployment, but it is limited by a lack of understanding of air traffic associated uncertainties, specifically contextual factors. Trajectory predictors are usually based on modelling aircraft dynamics based on intrinsic aircraft features. These aircraft operate within a known air route structure and under given meteorological conditions. However, actual aircraft trajectories are modified by the air traffic control depending on potential conflicts with other traffics. This paper introduces surrounding air traffic as a feature for ground-based trajectory prediction. The introduction of air traffic as a contextual factor is addressed by identifying aircraft which are likely to lose the horizontal separation. For doing so, this paper develops a probabilistic horizontal interdependency measure between aircraft supported by machine learning algorithms, addressing time separations at crossing points. Then, vertical profiles of flight trajectories are characterised depending on this factor and other intrinsic features. The paper has focused on the descent phase of the trajectories, using datasets corresponding to an en-route Spanish airspace volume. The proposed interdependency measure demonstrates to identify in advance conflicting situations between pairs of aircraft for this use case. This is validated by identifying associated air traffic control actions upon them and their impact on the vertical profile of the trajectories. Finally, a trajectory predictor for the vertical profile of the trajectory is developed, considering the interdependency measure and other operational factors. The paper concludes that the air traffic can be included as a factor for the trajectory prediction, impacting on the location of the top of descent for the specific case which has been studied

Hierarchical Bayesian Models to Estimate the Number of Losses of Separation between Aircraft in Flight

Air transport is considered to be the safest mode of mass transportation. Air traffic management (ATM) systems constitute one of the fundamental pillars that contribute to these high levels of safety. In this paper we wish to answer two questions: (i) What is the underlying safety level of ATM systems in Europe? and (ii) What is the dispersion, that is, how far does each ATM service provider deviate from this underlying safety level? To do this, we develop four hierarchical Bayesian inference models that allow us to infer and predict the common rate of occurrence of SMIs, as well as the specific rates of occurrence for each air navigation service provider (ANSP). This study shows the usefulness of hierarchical structures when it comes to obtaining parameters that enable risk to be quantified effectively. The models developed have been found to be useful in explaining and predicting the safety performance of 29 European ATM systems with common regulations and work procedures, but with different circumstances and numbers of aircraft, each managing traffic of differing complexity

European Universities Initiative: How Universities May Contribute to a More Sustainable Society

The European Universities initiative, launched by the European Commission in 2018, has its origin in the concept of Civic Universities (CivUs) and consists of transnational higher education alliances throughout the European Union that share long-term strategies. They are expected to become universities of the future, to promote European ideals and character, and to revolutionize the competitiveness and excellence of European higher education. European universities add 41 alliances, involving 31 different countries. This article presents an early quantitative evaluation of this initiative. This paper addresses the coverage of the 41 alliances and selects five of the most advanced for a deeper evaluation of their best practices and their contribution to the realization of CivUs. This paper also outlines the criteria for evaluating the extent to which good practices implemented by these alliances are aligned and can contribute to the attributes of CivUs, based upon state-of-the-art educational standards. A quantitative framework, based on application of the analytical hierarchy process (AHP), is also provided to rank the good practices developed by these alliances against the previous evaluation criteria. Furthermore, by applying a sensitivity analysis, this paper also addresses the robustness of this approach

Tactical runway scheduling for demand and delay management

Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making (A-CDM) programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management. By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, we present a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. Our model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach

Finding precursory air traffic management safety metrics using exploration of trajectory radar tracks

The definition of a set of precursory safety metrics is critical to detect when an airspace is degrading in terms of safety and thus undesired effects are becoming more likely. Furthermore, safety metrics are paramount in the measurement of the impact of new operational procedures or technical improvements in the air traffic control system. The study presented in this paper introduces three safety metrics (reaction time performance indicator, time to closest point of approach performance indicator, and time to closest point of approach critical limit ratio) derived from a given airspace and a sizable, assorted traffic sample extracted from traffic surveillance track data. The metrics are used to characterize the airspace as a function of the safety outcome, which can be continuously overseen. The final goal of the safety metrics is to be used as an airspace safety warning system, where precursory metrics would signal the need to act to maintain the air traffic control system safety target in the face of operational, organizational, technical, or legal changes