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The purpose of this paper is to focus on the development of conflict-resolution algorithms between Remotely Piloted Aircraft System (RPAS) and conventional aircraft. The goal of the conflict-resolution algorithm is to estimate the minimum protection distance (MPD) which is required to avoid a potential conflict.

The conflict-resolution algorithms calculate the last location at which an RPAS must start climbing to avoid a separation minima infringement. The RPAS maneuvers to prevent the conventional aircraft based on the kinematic equations. The approach selects two parameters to model the conflict-geometry: the path-intersection angle and the Rate of Climb (ROCD).

Results confirmed that the aircraft pair flying in opposition was the worst scenario because the MPD reached its maximum value. The best value of the MPD is about 12 Nautical Miles to ensure a safe resolution of a potential conflict. Besides, variations of the ROCD concluded that the relation between the ROCD and the MPD is not proportional.

The primary limitation is that the conflict-resolution algorithms are designed in a theoretical framework without bearing in mind other factors such as communications, navigation capacity, wind and pilot errors among others. Further work should introduce these concepts to determine how the MPD varies and affects air traffic safety. Moreover, the relation between an ROCD requirement and the MPD will have an impact on regulations.

The non-linear relation between the MPD and the ROCD could be the pillar to define a standardized MPD in the future for RPAS systematic integration. To accomplish this standard, RPAS could have to fulfil a requirement of minimum ROCD until a specified flight level.

This paper is the first approach to quantify the Minimum Protection Distance between RPAS and conventional aircraft, and it can serve the aeronautical community to define new navigation requirements for RPAS.

This paper aims to assess the implications in safety levels by the integration of remotely piloted aircraft system (RPAS). The goal is to calculate the number of RPAS that can jointly operate with conventional aircraft regarding conflict risk, without exceeding current safety levels.

This approach benchmarks a calculated level of safety (CLS) with a target level of safety (TLS). Monte Carlo (MC) simulations quantify the TLS based on the current operation of conventional aircraft. Then, different experiments calculate the CLS associated with combinations of conventional aircraft and RPAS. MC simulations are performed based on probabilistic distributions of aircraft performances, entry times and geographical distribution. The safety levels are based on a conflict risk model because the safety metrics are the average number of conflicts and average conflict duration.

The results provide restrictions to the number of RPAS that can jointly operate with conventional aircraft. The TLS is quantified for four conventional aircraft. MC simulations confirm that the integration of RPAS demands a reduction in the total number of aircraft. The same number of RPAS than conventional aircraft shows an increase over 90% average number of conflicts and 300% average conflict time.

The methodology is applied to one flight level of en-route airspace without considering climbing or descending aircraft.

This paper is one of the most advanced investigations performed to quantify the number of RPAS that can be safely integrated into non-segregated airspace, which is one of the challenges for the forthcoming integration of RPAS. Particularly, Europe draws to allow operating RPAS and conventional aircraft in non-segregated airspace by 2025, but this demanding perspective entails a thorough analysis of operational and safety aspects involved.

The use of the 4D trajectory operational concept in the future air traffic management (ATM) system will require the aircraft to meet very accurately an arrival time over a designated checkpoint. To do this, time intervals known as time windows (TW) are defined. The purpose of this paper is to develop a methodology to characterise these TWs and to manage the uncertainty associated with the evolution of 4D trajectories.

4D trajectories are modelled using a point mass model and EUROCONTROL’s BADA methodology. The authors stochastically evaluate the variability of the parameters that influence 4D trajectories using Monte Carlo simulation. This enables the authors to delimit TWs for several checkpoints. Finally, the authors set out a causal model, based on a Bayesian network approach, to evaluate the impact of variations in fundamental parameters at the chosen checkpoints.

The initial results show that the proposed TW model limits the deviation in time to less than 27 s at the checkpoints of an en-route segment (300 NM).

The objective of new trajectory-based operations is to efficiently and strategically manage the expected increase in air traffic volumes and to apply tactical interventions as a last resort only. We need new tools to support 4D trajectory management functions such as strategic and collaborative planning. The authors propose a novel approach for to ensure aircraft punctuality.

The main contribution of the paper is the development of a model to deal with uncertainty and to increase predictability in 4D trajectories, which are key elements of the future airspace operational environment.

The current air traffic management (ATM) operational approach is changing; “time” is now integrated as an additional fourth dimension on trajectories. This notion will impose on aircraft the compliance of accurate arrival times over designated checkpoints (CPs), called time windows (TWs). This paper aims to clarify the basic requirements and foundations for the practical implementation of this functional framework.

This paper reviews the operational deployment of 4D trajectories, by defining its relationship with other concepts and systems of the future ATM and communications, navigation and surveillance (CNS) context. This allows to establish the main tools that should be considered to ease the application of the 4D-trajectories approach. This paper appraises how 4D trajectories must be managed and planned (negotiation, synchronization, modification and verification processes). Then, based on the evolution of a simulated 4D trajectory, the necessary corrective measures by evaluating the degradation tolerances and conditions are described and introduced.

The proposed TWs model can control the time tolerance within less than 100 s along the passing CPs of a generic trajectory, which is in line with the expected future ATM time-performance requirements.

The main contribution of this work is the provision of a holistic vision of the systems and concepts that will be necessary to implement the new 4D-trajectory concept efficiently, thus enhancing performance. It also proposes tolerance windows for trajectory degradation, to understand both when an update is necessary and what are the conditions required for pilots and air traffic controllers to provide this update.

The purpose of this study is to establish a systematic framework to characterise the safety of air routes, in terms of separation minima infringements (SMIs) between en-route aircraft, based on the definition of models known as safety performance functions.

Techniques with high predictive capability were selected that enable both expert knowledge and data to be harnessed: Bayesian networks. It was necessary to establish a conceptual framework that integrates the knowledge currently available on the causality and precursors of SMIs with the hindsight derived from the analysis of the type of data available. To translate the conceptual framework into a set of causal subnets, the concepts of air traffic management (ATM) barrier model and event trees have been incorporated.

The model combines analytics and insights, as well as predictive capability, to answer the question of how airspace separation infringements are produced and what their frequency of occurrence will be. The main outputs of the network are the predicted probability of success for the ATM barriers and the predicted probability distribution of the vertical and horizontal separation of an aircraft in its closest point of approach.

The main contribution of this work is that, by virtue of the calculation capacity obtained, the network can be used to draw conclusions about the impact that a modification of the airspace and of the traffic, or operational conditions, would have on the effectiveness of the barriers and on the final distributions of distance between aircraft in the CPA, thereby estimating the probability of SMI.

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. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage.

By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations.

This 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: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work.

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 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.

The purpose of this paper is to set out a methodology for characterising the complexity of air traffic control (ATC) sectors based on individual operations. This machine learning methodology also learns from the data on which the model is based.

The methodology comprises three steps. Firstly, a statistical analysis of individual operations is carried out using elementary or initial variables, and these are combined using machine learning. Secondly, based on the initial statistical analysis and using machine learning techniques, the impact of air traffic flows on an ATC sector are determined. The last step is to calculate the complexity of the ATC sector based on the impact of its air traffic flows.

The results obtained are logical from an operational point of view and are easy to interpret. The classification of ATC sectors based on complexity is quite accurate.

The methodology is in its preliminary phase and has been tested with very little data. Further refinement is required.

The methodology can be of significant value to ATC in that when applied to real cases, ATC will be able to anticipate the complexity of the airspace and optimise its resources accordingly.

This paper aims to analyse remotely piloted aircraft system (RPAS) integration in non-segregated terminal airspace. This work aims to identify the potential airspace volumes where a free operation of RPAS can be developed by analysing the airspace design of the terminal airspace.

The methodology considers five crucial elements of the airspace design: obstacles, prohibited, restricted and dangerous zones, aerodrome zones, departing and arriving procedures and visual corridors. Free operation of RPAS is performed in those airspace volumes that no interaction with instrumental flight rules (IFR) flights is expected. Free RPAS airspace volumes are separated through current IFR separation minima.

The results show there is a significant amount of available airspace that RPAS can operate without interaction with conventional aircraft. The more significant risks are allocated by the limitations imposed by departing and arriving procedures in the terminal airspace.

The methodology is applied to medium-dense terminal airspace. This work assumes RPAS can perform visual or instrumental flights.

RPAS is a capital issue for the majority of aviation actors. This work underlay the further development of a methodology regarding airspace design for RPAS in a terminal control area.

Air traffic controllers (ATCOs) play a fundamental role in the safe, orderly and efficient management of air traffic. In the interests of improving safety, it would be beneficial to know what the workload thresholds are that permit ATCOs to carry out their functions safely and efficiently. The purpose of this paper is to present the development of a simulation platform to be able to validate an affective-cognitive performance methodology based on neurophysiological factors applied to ATCOs, to define the said thresholds.

The process followed in setting up the simulation platform is explained, with particular emphasis on the design of the program of exercises. The tools designed to obtain additional information on the actions of ATCOs and how their workload will be evaluated are also explained.

To establish the desired methodology, a series of exercises has been designed to be simulated. This paper describes the project development framework and validates it, taking preliminary results as a reference. The validation of the framework justifies further study to extend the preliminary results.

This paper describes the first part of the project only, i.e. the definition of the problem and a proposed methodology to arrive at a workable solution. Further work will concentrate on carrying out a program of simulations and subsequent detailed analysis of the data obtained, based on the conclusions drawn from the preliminary results presented.

The methodology will be an important tool from the point of view of safety and the work carried out by ATCOs. This first phase is crucial as it provides a solid foundation for later stages.

This study aims to examine how Brazilian universities have implemented the sustainable development goals (SDGs) in institutional practices, curriculum and teaching, management and community outreach programs.

This study is based on content analysis of the data sets of Brazilian higher education courses and their link to SDGs. A literature review has also been developed to integrate the knowledge of sustainability practices in Brazilian universities. Dashboards will be used to present the related findings.

Brazilian universities’ practices have concentrated their efforts on SDGs, which include responsible consumption and production, sustainable cities and communities and quality education. The literature emphasizes energy efficiency, reflecting its prominence as a subject of study among universities.

This research highlights the innovative use of data analysis techniques to present the findings. It also provides examples of environmental, social and governance (ESG) practices, frameworks and tools that can help sustainability management in this sector.