Under the European Union’s Green Deal, air traffic management is tasked with achieving incremental CO2 reductions of up to 6% by 2050. Among the instruments available under Regulation (EU) 2019/317 is the modulation of en-route charges, the fees paid by airlines for air navigation services, which may be applied with the stated objective of "reducing the environmental impact of flying." Although prior research has explored charge modulation for demand-capacity imbalances management, its systematic application for CO2 reduction remains largely unexplored. This paper introduces the Trajectory-Based Modulation of RouteCharges (TBMRC) model, a strategic framework designed to use route charge modulation to address environmental objectives alongside demand-capacity imbalance. In principle, airlines seek to minimise costs, favouring the shortest trajectories. In practice, differences in national charge rates and airspace congestion can create unintended incentives for longer routes. The TBMRC model corrects these incentives by modulating charges on a per-trajectory basis and assigning minimal strategic time shifts. This aligns airline cost-minimisation behaviour with system-wide emission reductions and demand-capacity imbalances mitigation, while respecting the revenue-neutrality principle for Air Navigation Service Providers (ANSPs). Tohandle the computational complexity of yearly traffic optimisation, TBMRC decomposes the problem into three sequential linear programming steps. Applied to real traffic data, the model proved the capability to eliminate the great majority of unnecessary detours, substantially reduce demand-capacity imbalances, while leaving ANSP revenue-to-workload ratios nearly unchanged. This work provides a novel proof-of-concept for an integrated economic-environmental strategy in ATM and outlines the policy and implementation challenges for its realisation.

The Mercury simulator, a performance assessment platform, is a stochastic, agent-based model developed over several years during research projects. It features a detailed description of the air transportation system at the European level, including passengers and aircraft, and various important actors such as the Network Manager, airports, etc. This article presents the possibilities offered by the simulator’s current, now open-source version. We describe the core Mercury functionalities and highlight its modularity and the possibility of its usage with other tools. We present a new interface, which supports user-friendly interaction with the simulator, exploring data input/output and parameter settings. We emphasise possible uses as a solution performance assessment tool, which is usable early in the innovation pipeline to better estimate the impact of changes and new systems in the air transportation system. We hope that opening the simulator may encourage other developers to open their models, allowing faster prototyping of new operational concepts early in the innovation pipeline and an in fine support standardisation and higher performance of simulation-based performance assessment tools.

The current and forecast air traffic levels lead to demand-capacity imbalances, which are dealt with by delaying flights through the allocation of air traffic flow management (ATFM) slots. To mitigate the delay impact on airspace users (AUs) and passengers, User Driven Prioritisation Process (UDPP) solutions are under development, with the goal to enhance flexibility for airlines to prioritise their own flights in the ATFM regulations. UDPP solutions are developed in collaboration with AUs, achieving high maturity level and even operational use at some airports. While UDPP solutions in reality are still based on manual or semi-automated procedures, in this paper we show that when an airline has an accurate delay cost model at disposal, the prioritisation process can be fully automated via an integer programming model that provides the prioritisation that optimises the AUs' UDPP exploitation. We use this automated process and the implementation of the UDPP mechanism to provide an estimation of the benefits of UDPP in terms of cost with respect to the current ATFM regulation process.

See detailed reviews and responses in the PDF file. DOI for the original paper: https://doi.org/10.59490/joas.2023.7223

The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, which are usually of interest in policy analysis. Our experiments show that XALM’s predictive performance is comparable to that of the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels.Using the ‘Mercury’ (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager’s range and scope by analysing six variables. This case study illustrates the effectiveness of the proposed framework in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, it complements traditional simulation-based analyses.Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.

Air Traffic Flow Management (ATFM) measures are often used to alleviate capacity demand imbalances. Usually, these measures impose a departure delay on flights crossing the airspace in question, where delay is assigned on a "First Planned First Served" (FPFS) base, which minimises total delay. Since delays typically have a different impact on individual flights in terms of cost, a procedure based on cost minimisation could reduce delay-related costs. User-Driven Prioritisation Process (UDPP) is developing a set of solutions aimed at allowing airlines to rearrange their flights within their own slots assigned by the FPFS rule. Inter-airline cost reducing approaches are still missing, but several works have been launched in this direction, using either central optimisations or market-based mechanisms. We analyse the impact of cost approximations procedures, integral part of certain inter-airline mechanisms, overall likely to be used by airlines or the Network Manager (that manages ATFM measures). Using cost models and simulations to collect the true cost of delay profiles, we show that the impact of cost approximations have been severely underestimated, leaving little room for new mechanisms to improve over UDPP. Moreover, we show that the errors made by the airlines on their own costs, expected at least for some airlines, further deteriorate the situation, including UDPP. However, we find that approximation procedures create a strong resilience to these errors, showing how both UDPP and inter-airline procedures may benefit from not having the airlines communicate their detailed costs. Thus, we find that any design of new mechanisms could include a cost approximation procedure in order to increase its resilience.

This article explores the potential impact of short-haul flight bans in Spain. We build the rail and flight network for the Spanish peninsula, merging openly available ADS-B-based data, for the reconstruction of air schedules and aircraft rotations, and rail operator data, for the modelling of the rail network. We then simulate a ban that would remove flights having a suitable train replacement, \ie representing a trip shorter than a threshold that we vary continuously up to 15-h. We study the impact in terms of 1) air route reduction, 2) aircraft utilisation and fleet downsizing for airlines, 3) airport infrastructure relief and rail network requirements, 4) CO2 emissions, and 5) possible itineraries and travel times for passengers. We find that a threshold of 3 hours (banning all flights with a direct rail alternative faster than three hours) presents some notable advantages in emissions while keeping the aircraft utilisation rate at an adequate level. Interestingly, the passengers would then experience an increase in their itinerary options, with only a moderate increase in their total travelling times.

This White Paper sets out to explain the value that metamodelling can bring to air traffic management (ATM) research. It will define metamodelling and explore what it can, and cannot, do. The reader is assumed to have basic knowledge of SESAR: the Single European Sky ATM Research project. An important element of SESAR, as the technological pillar of the Single European Sky initiative, is to bring about improvements, as measured through specific key performance indicators (KPIs), and as implemented by a series of so-called SESAR 'Solutions'. These 'Solutions' are new or improved operational procedures or technologies, designed to meet operational and performance improvements described in the European ATM Master Plan.

The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, those usually of interest in policy analysis. Our experiments show XALM's predictive performance comparable to the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels. Using the `Mercury' (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager's range and scope by analysing six variables. This case study illustrates XALM's effectiveness in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, XALM complements traditional simulation-based analyses. Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.

This article highlights the importance of uncertainty in day-to-day operations, and the need to take it into account to properly assess the cost of delay for airspace users. It defines a cost of uncertainty and estimates it using real data. It provides some easily computable models based on the average and standard deviation of delay to estimate the cost of delay in general. The article shows that uncertainty is also important in the formulation of buffers for airlines and provides a simple model to estimate the optimal assignment, further using real data to compute the optimal value at different airports.

This article presents some of the results on the implementation of a decentralised delay management process that we call 4D trajectory adjustments (4DTA) obtained with Mercury, a stochastic agent-based model. The model operates within a strong agent paradigm at the level of individual flights and passengers. It includes a realistic cost model for the airlines, allowing us to have a good tactical choice model and excellent estimation of airspace user costs. Due to the inclusion of different stakeholders, including passengers, and various processes – like aircraft turnaround or passenger reaccommodation – it is able to catch European-wide network effects that are inaccessible to other models. It was used to study the 4DTA process, a blend of ‘wait for passengers and tactical speed adjustments’, which is shown to have a significant impact on the system. Thanks to the detailed output of the model, we are able to breakdown the effect for different classes of flights and passengers, and show that important trade-offs exist in terms of delays and costs. In particular, the introduction of such mechanism could be detrimental to non-connecting passengers, especially at secondary airports.

This paper presents results from the SESAR ER3 Domino project. Three mechanisms are assessed at the ECAC-wide level: 4D trajectory adjustments (a combination of actively waiting for connecting passengers and dynamic cost indexing), flight prioritisation (enabling ATFM slot swapping at arrival regulations), and flight arrival coordination (where flights are sequenced in extended arrival managers based on an advanced cost-driven optimisation). Classical and new metrics, designed to capture network effects, are used to analyse the results of a micro-level agent-based model. A scenario with congestion at three hubs is used to assess the 4D trajectory adjustment and the flight prioritisation mechanisms. Two different scopes for the extended arrival manager are modelled to analyse the impact of the flight arrival coordination mechanism. Results show that the 4D trajectory adjustments mechanism succeeds in reducing costs and delays for connecting passengers. A trade-off between the interests of the airlines in reducing costs and those of non-connecting passengers emerges, although passengers benefit overall from the mechanism. Flight prioritisation is found to have no significant effects at the network level, as it is applied to a small number of flights. Advanced flight arrival coordination, as implemented, increases delays and costs in the system. The arrival manager optimises the arrival sequence of all flights within its scope but does not consider flight uncertainties, thus leading to sub-optimal actions.

The development of trajectory-based operations and the rolling network operations plan in European air traffic management network implies a move towards more collaborative, strategic flight planning. This opens up the possibility for inclusion of additional information in the collaborative decision-making process. With that in mind, we define the indicator for the economic risk of network elements (e.g., sectors or airports) as the expected costs that the elements impose on airspace users due to Air Traffic Flow Management (ATFM) regulations. The definition of the indicator is based on the analysis of historical ATFM regulations data, that provides an indication of the risk of accruing delay. This risk of delay is translated into a monetary risk for the airspace users, creating the new metric of the economic risk of a given airspace element. We then use some machine learning techniques to find the parameters leading to this economic risk. The metric is accompanied by an indication of the accuracy of the delay cost prediction model. Lastly, the economic risk is transformed into a qualitative economic severity classification. The economic risks and consequently economic severity can be estimated for different temporal horizons and time periods providing an indicator which can be used by Air Navigation Service Providers to identify areas which might need the implementation of strategic measures (e.g., resectorisation or capacity provision change), and by Airspace Users to consider operation of routes which use specific airspace regions.

In ATM systems, the massive number of interacting entities makes it difficult to identify critical elements and paths of disturbance propagation, as well as to predict the system-wide effects that innovations might have. To this end, suitable metrics are required to assess the role of the interconnections between the elements and complex network science provides several network metrics to evaluate the network functioning. Here we focus on centrality and causality metrics measuring, respectively, the importance of a node and the propagation of disturbances along links. By investigating a dataset of US flights, we show that existing centrality and causality metrics are not suited to characterise the effect of delays in the system. We then propose generalisations of such metrics that we prove suited to ATM applications. Specifically, the new centrality is able to account for the temporal and multi-layer structure of ATM network, while the new causality metric focuses on the propagation of extreme events along the system.

When a flight’s operational conditions change (e.g., an updated weather forecast), various alternative trajectories may be computed. These usually require trade-offs between expected fuel burn and delay. The pilot, or the dispatcher, considers these expected values to decide how to operate the flight. This approach has two main challenges. Firstly, it requires the translation of arrival delay into parameters that are relevant for the airline (on-time performance and cost of delay). Secondly, uncertainties in the system need to be estimated (e.g., holding at arrival). These estimations rely on airline staff expertise. Pilot3 sets out to overcome these issues by developing a new, multi-criteria decision-support tool, which incorporates explicit estimators for performance indicators and ATM operational parameters. These estimators will be developed incrementally, from simple heuristics to advanced machine-learning models, building on previous experience.

This article presents a new, holistic model for the air traffic management system, built during the Vista project. The model is an agent-driven simulator, featuring various stakeholders such as the Network Manager and airlines. It is a microscopic model based on individual passenger itineraries in Europe during one day of operations. The article focuses on the technical description of the model, including data and calibration issues, and presents selected key results for 2035 and 2050. In particular, we show clear trends regarding emissions, delay reduction, uncertainty, and increasing airline schedule buffers.

This deliverable presents the improvement planned to be performed until the end of the project regarding the model (implementation changes, recalibration and the simulation outputs), plus the metrics and scenarios that will be re-run with the model. These changes are based on the insights gathered through the analysis activities performed in the scope of investigative case studies (see D3.2 Investigative case studies description and D5.2 Investigative case studies results) and the feedback obtained from experts and stakeholders on the different workshops activities performed (see D6.3 Workshop results summary). These insights highlighted missing features of the model and potential improvements, as well as some gaps and shortcomings. The scenarios for this analysis have been chosen highly selectively in order to prioritise the depth of the analysis and methodology development over a large number of scenarios, as these have already been analysed in the scope of the investigative case studies.

This deliverable presents the results from the analysis of the model executing the investigative case studies. The document focuses on the validation activities and the results for the three mechanisms modelled in Domino in the unitary case studies. The three mechanism are: 4D Trajectory Adjustment, which focuses on the use of dynamic cost indexing and wait-for-passengers rules; Flight Prioritisation, which considers the possibility of slot swapping at ATFM regulations; and Flight Arrival Coordination, which models different optimisation approaches E-AMAN could consider. Each mechanism has three levels of implementation: Level 0 (with current capabilities), Level 1 (with more advanced features) and Level 2 (more explorative). The traffic is set on a given day (12 September 2014) considering flights and passengers’ itineraries. Two levels of delay are considered: default and stressed. In total 14 scenarios have been modelled and analysed. This deliverable presents the use of classical and network metrics (centrality and causality) on the outcome of the whole European level agent-based model. The model still requires further development and adjustment, but results show that it is already capable of capturing complex interactions among the ATM elements. Finally, the network metrics are already presenting their potential to capture non-direct interactions between elements in the system. The results have been shared with experts and airspace users at two workshops. The feedback obtained and the results of the analysis and validation activities will be considered for the final version of Domino.

This deliverable presents the metrics proposed to assess the impact of innovations in the ATM system and a stylized ABM model, called a ‘toy model’, to be used as a test ground for the metrics. Existing network metrics are reviewed and their limitations are highlighted by applying them to real data. New metrics are then suggested to overcome these limitations. Their better results in measuring interconnections and causal relationships between the elements of the ATM system are shown for empirical case studies. The design of the toy model is presented and preliminary results of its baseline implementation are shown.

We present a simplified model of the strategic allocation of trajectories in a generic airspace for commercial flights. In this model, two types of companies, characterized by different cost functions and different strategies, compete for the allocation of trajectories in the airspace. With an analytical model and numerical simulations, we show that the relative advantage of the two populations—companies—depends on external factors like traffic demand as well as on the composition of the population. We show that there exists a stable equilibrium state which depends on the traffic demand. We also show that the equilibrium solution is not the optimal at the global level, but rather that it tends to favour one of the two business models—the archetype for low-cost companies. Finally, linking the cost of allocated flights with the fitness of a company, we study the evolutionary dynamics of the system, investigating the fluctuations of population composition around the equilibrium and the speed of convergence towards it. We prove that in the presence of noise due to finite populations, the equilibrium point is shifted and is reached more slowly.