Network-wide assessment of 4D trajectory adjustments using an agent-based model

This paper presents results from the SESAR ER3 Domino project. It focuses on an ECAC-wide assessment of two 4D-adjustment mechanisms, implemented separately and conjointly. These reflect flight behaviour en-route and at-gate, optimising given (cost) objective functions. New metrics designed to capture network effects are used to analyse the results of a microscopic, agent based model. The results show that some implementations of the mechanisms allow the protection of the network from ‘domino’ effects. Airlines focusing on costs may trigger additional side-effects on passengers, displaying, in some instances, clear trade-offs between passenger- and flight-centric metrics

Strategic allocation of flight plans in air traffic management: an evolutionary point of view

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, characterised 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

NOSTROMO - D5.1 - ATM Performance Metamodels - Preliminary Release

This deliverable presents the results obtained with the meta-modelling process presented in D3.1 and D3.2 applied to the two micromodels (or simulators), Mercury and FLITAN, themselves implementing concepts from four SESAR solutions, PJ01.01, PJ07.02, PJ08-01, and PJ02.08. The objective of the meta-modelling process is explained briefly again in the introduction, in particular with respect to performance assessment. The rationale for the selection of the SESAR solutions implemented in the simulators are briefly explained too. The simulators are presented in two distinct chapters. First, a general presentation of each simulator is given, with past challenges and development, before explaining the development steps carried out to implement the concepts from the chosen solutions. Domain research questions that could be answered by these implementations are highlighted along the way. The meta-modelling process is then briefly explained again, followed by the results obtained with the two simulators, in distinct sections. The results highlight the performance of the meta-model with respect to approximating the output of the micromodels, but not the performance of the models themselves with respect to the research questions, which will be explored in WP7 instead. The deliverable closes with some considerations on the meta-modelling performance and next steps for this line of work

New centrality and causality metrics assessing air traffic network interactions

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

Towards new metrics assessing air traffic network interactions

In ATM systems, the massive number of interactin entities makes it difficult to predict the system-wide effects that innovations might have. Here, we present the approach proposed by the project Domino to assess such effects and identify the impact that innovations might bring for the different stake-holders, based on agent-based modelling and complex network science. Domino will model scenarios mirroring different system innovations which change the agents’ actions and behaviour. Suitable network metrics are needed to evaluate the effect of innovations on the network functioning. We review existing centrality and causality metrics and show their limitations in characterising the network by applying them to a dataset of US flights. We finally suggest improvements that should be introduced to obtain new metrics answering to Domino’s needs

Active Learning Metamodels for ATM Simulation Modeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables and their interrelationships, unknown stochastic phenomena, and ultimately human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their obvious advantages,simulation models can still end up being quite complex themselves. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves laborious and systematic analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values

Active Learning for Air Traffic Management Simulation Metamodeling

Transportation systems are particularly prone to exhibiting overwhelming complexity on account of the numerous involved variables, corresponding interrelationships, and the unpredictability of human behavior. Simulation approaches are commonly used tools to describe and study such intricate real-world systems. Despite their clear advantages, these models can too suffer from high complexity and computational hindrances, especially when designed along with fine detail. The field of Air Traffic Management (ATM) modeling is no stranger to such concerns, as it traditionally involves exhausting and manual-driven intense analyses built upon computationally heavy simulation models. This rather frequent shortcoming can be addressed by employing simulation metamodels combined with active learning strategies to approximate, via fast functions, the input-output mappings inherently defined by the simulation models in an efficient way. In this work, we propose an exploration framework that integrates active learning and simulation metamodeling in a single unified approach to address recurrent computational bottlenecks typically associated with intense performance impact assessments within the field of ATM. Our methodology is designed to systematically explore the simulation input space in an efficient and self-guided manner, ultimately providing ATM practitioners with meaningful insights concerning the simulation models under study. Using a fully developed state-of-the-art ATM simulator and employing a Gaussian Process as a metamodel, we show that active learning is indeed capable of enhancing both the modeling and performances of simulation metamodeling by strategically avoiding redundant computer experiments and predicting simulation outputs values given a pre-specified input region

Explainable Metamodels for ATM Performance Assessment

Fast-time simulation constitutes a well-known and long-established technique within the Air Traffic Management (ATM) community. However, it is often the case that simulation input and output spaces are underutilized, limiting the full understandability, transparency, and interpretability of the obtained results. In this paper, we propose a methodology that combines simulation metamodeling and SHapley Additive exPlanations (SHAP) values, aimed at uncovering the intricate hidden relationships among the input and output variables of a simulated ATM system in a rather practical way. Whereas metamodeling provides explicit functional approximations mimicking the behavior of the simulators, the SHAP-based analysis delivers a systematic framework for improving their explainability. We illustrate our approach using a state-of-the-art ATM simulator across two case studies in which two delay-centered performance metrics are analyzed. The results show that the proposed methodology can effectively make simulation and its results more explainable, facilitating the interpretation of the obtained emergent behavior, and additionally opening new opportunities towards novel performance assessment processes within the ATM research field

Best practice in undertaking and reporting health technology assessments : Working Group 4 report

[Executive Summary] The aim of Working Group 4 has been to develop and disseminate best practice in undertaking and reporting assessments, and to identify needs for methodologic development. Health technology assessment (HTA) is a multidisciplinary activity that systematically examines the technical performance, safety, clinical efficacy, and effectiveness, cost, costeffectiveness, organizational implications, social consequences, legal, and ethical considerations of the application of a health technology (18). HTA activity has been continuously increasing over the last few years. Numerous HTA agencies and other institutions (termed in this report “HTA doers”) across Europe are producing an important and growing amount of HTA information. The objectives of HTA vary considerably between HTA agencies and other actors, from a strictly political decision making–oriented approach regarding advice on market licensure, coverage in benefits catalogue, or investment planning to information directed to providers or to the public. Although there seems to be broad agreement on the general elements that belong to the HTA process, and although HTA doers in Europe use similar principles (41), this is often difficult to see because of differences in language and terminology. In addition, the reporting of the findings from the assessments differs considerably. This reduces comparability and makes it difficult for those undertaking HTA assessments to integrate previous findings from other HTA doers in a subsequent evaluation of the same technology. Transparent and clear reporting is an important step toward disseminating the findings of a HTA; thus, standards that ensure high quality reporting may contribute to a wider dissemination of results. The EUR-ASSESS methodologic subgroup already proposed a framework for conducting and reporting HTA (18), which served as the basis for the current working group. New developments in the last 5 years necessitate revisiting that framework and providing a solid structure for future updates. Giving due attention to these methodologic developments, this report describes the current “best practice” in both undertaking and reporting HTA and identifies the needs for methodologic development. It concludes with specific recommendations and tools for implementing them, e.g., by providing the structure for English-language scientific summary reports and a checklist to assess the methodologic and reporting quality of HTA reports

NOSTROMO - D5.2 - ATM Performance Metamodels - Final Release

This deliverable presents the third iteration of the development of the two micromodels Flitan and Mercury and the results obtained with them with the active learning process, as described in the deliverables D3.X. In this iteration, Flitan implemented concepts from PJ08.01 and PJ02.08, and Mercury implemented a module related to PJ07.02. Mercury also developed an additional module related to PJ01.01, which description is presented in Annex only, since no results could be produced in time with it for this deliverable. The development is presented in two different chapters for each simulator, with general descriptions referred to from D5.1. The modules related to each SESAR solution are described separately. The latest version of the meta-modelling process is described briefly, followed by the results obtained with the two simulators, in distinct sections. This chapter shows the performance of the meta-model with respect to approximating micro simulators