Data-driven methodology for uncertainty quantification of aircraft trajectory predictions

This work present a framework based on datadriven techniques for quantifying and chaos theory for propagating the uncertainty present in the aircraft trajectory prediction process when computing the expected trajectory from a given flight plan. The developed framework employs data assimilation models to capture real-time information from the air traffic system and introduces a novel methodology in order to account for the uncertainty of the weather conditions. The comparison of the resulting set of probabilistic trajectories and the actually flown ones proves how the former could be a key enabler to support envisioned trajectory-based operation concepts and modern airline operations planning.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

ATM network modelling, uncertainty propagation with thunderstorm disruptions

In this work, as a part of START, we have developed an ATM network macro-model, allowing us to model the propagation of flight trajectory uncertainties and further assess the impact of disruptive events, i.e., thunderstorms. We utilized data-driven analytics models mimicking the dynamics of epidemic spreading, which is analogous to delay or uncertainty propagation over transport networks. The connections between the operational aspects of the air traffic flow management and the developed meta-model are given as the airports' traffic densities correlated with the infection rates among the individuals; and the capability to absorb the uncertainties of the airports associated with recovery rates. Uncertainties over individual flight trajectories, which are the functions of flight times, have been defined through probabilistic distributions where superposed on the arrival times. Deep learning models have been integrated to capture the nonlinear relationship between the recovery rates, uncertainty accumulation, and disruptive events' attributes. The model allowed us to simulate and analyze the behavior of the network under uncertainty accumulations coming from trajectory uncertainty. Finally, we have used Reinforcement Learning to explore the best actions to enhance the network resiliency, defined through stability theory. From the operational perspective, resiliency is associated with the managing balance between the intervention rate (depending on "the time for washing away the effect of the transition period) and costs. The problem, at this point, transformed into an optimization-based control problem to guarantee convergence over time, meaning the effect of disruptive events dies out eventually. Quick recovery is typically preferred, but it applies significant intervention measures impacting many flights in this case. RL provided us with pinpointing the OD pairs, and the flights require regulatory action such as flight cancelation and aircraft grounding. The case studies are analyzed for the selected time windows chosen in the interval of 1-10 June 2018, where thunderstorms affected large areas of North-West Europe with intense local convective activities

Data-driven uncertainty quantification and propagation for probabilistic trajectory planning

One of the main objectives of Trajectory-Based Operations (TBO) is to increase the predictability of the aircraft behavior within the Air Traffic Management (ATM) system. However, most systems involved in TBO (such as flight planning systems) focus on proposing deterministic trajectories in the strategic phase, not taking into account the uncertainty factors that affect the trajectory prediction process in the tactical phase. Consequently, there is an increased frequency of updates and modifications to trajectories in later planning phases, which leads to degraded stability, resulting in an overall decrease of the performance of the ATM network. In this presentation, a data-driven methodology will be introduced for characterizing the uncertainties affecting the development of an aircraft trajectory, together with their integration into a stochastic trajectory predictor for obtaining robust sets of probabilistic trajectories from an initial flight plan. Additionally, this methodology employs data assimilation models that capture updated information from the air traffic system to reduce the present uncertainty. First, the main sources of uncertainty for aircraft trajectories will be identified and quantified using historical flight instances for a full year of pan-European air traffic. After quantifying these sources of uncertainty, it will be possible to evaluate the potential variations for a flight plan given the probability distributions for uncertain factors affecting the flight. Instead of applying computationally demanding methods, such as Monte Carlo simulations, for calculating all possible trajectories, a stochastic trajectory predictor is proposed that makes use of the characterization of trajectory uncertainty to compute probabilistic trajectories given an initial flight plan. The stochastic trajectory predictor uses arbitrary Polynomial Chaos Expansion (PCE) theory and the point collocation method to find polynomials describing the aircraft trajectory for the initial flight plan as a function of the identified uncertain factors. Therefore, the quantified uncertainty sources can be fitted in the polynomials to find a reduced set of probabilistic trajectories that are robust and resilient to potential variations in the tactical phase. Complementing this, a set of advanced data-assimilation models based on machine learning techniques are integrated to provide accurate estimations for some of the uncertain factors based on the last available status of the air traffic system. These estimates reduce the uncertainty spectrum for important variables in the trajectory prediction process and help adapting the resulting probabilistic trajectories to the current system status. Finally, a study case is introduced in which the proposed methodology is implemented. This study includes the results of analyzing the probabilistic trajectories for one city-pair and supports the idea of integrating probabilistic trajectories as a key enabler for envisioned TBO concepts and modern airline operations plannin

Network-wide robust and resilient metaheuristic trajectory optimization under thunderstorm disruptions

Network-wide robust and resilient trajectory planning is realized after the uncertainty propagations at trajectory and ATM levels. The inputs are the 4D trajectories with uncertainty and the delays applied to trajectories for network resiliency. The delays only shift the trajectories in time. The output is a set of algorithmic solutions for optimal trajectory selection under high complexity situations. According to the START concept, a proposed rerouting and/or rescheduling solution of the user-preferred flight plan is proposed to improve the resiliency and robustness of overall planning. The optimization process is realized using the simulated annealing metaheuristic to find the optimal rerouting and delays for each flight. The objective function of this optimization problem is a complexity metric function, based on Linear Dynamical System. This metric can consider uncertainty in the 4D trajectories. However, the computation of such metric requires extensive computation time. We proposed GPU-based concept to speed up the metric computation. We have found that the proposed GPU-based concept can potentially provide the desired performance and prove the computational viability of the START project. Nevertheless, our findings are not uniformly positive, as the reliance on single-precision arithmetic (on which current GPUs provide substantially higher throughput) seems to have proved more problematic than our previous expectation. The global air traffic complexity is reduced by a factor of six hundred from around 120 to 0.2. It corresponds to a better organization of the traffic. In fact, the complexity is mainly due to very few flights. The complexity reduction decreases the potential number of conflicts, because there are less converging air traffic situations

Data-driven methodology for uncertainty quantification of aircraft trajectory predictions

One of the main objectives of the so-called trajectory-based operations (TBO) concept is to increase the predictability of the aircraft behavior within the air traffic management (ATM) system, thus reducing inefficiencies and increasing the robustness and resiliency of operations. Most systems involved in TBO, such as flight planning systems or on-ground trajectory predictors, focus on proposing deterministic trajectories in the strategic phase and do not take into account the uncertain factors that affect the trajectory prediction process. While TBO is enabled by the automated updating of trajectories in reaction to developing uncertainties, an excessive frequency of trajectory updates in later planning and tactical phases could lead to degraded stability, resulting in an overall decrease of the performance of the ATM network. The use of probabilistic trajectories instead of deterministic ones would allow to reduce the frequency of these updates, as well as increasing to increase the situational awareness of the ATM system. Nonetheless, the analysis of the uncertainty affecting the prediction of a flight is a complex problem that has been tackled in the literature. The main difficulty regarding aircraft trajectory uncertainty is that it cannot be estimated in a post-processing study based on the comparison between the predicted and the actual trajectories. This is because the uncertainty is represented by the estimation of those potential deviations in an a priori phase, based on the identification and quantification of the possible sources impacting that uncertainty and the propagation of the joint effect of those sources to obtain the probability distribution describing the potential trajectory

Towards a Stable and resilient ATM by integrating Robust airline operations into the network - Scientific Progress during the 1st year of START project.

Trajectory-based operations (TBO) is one of the cornerstones of a modernised air traffic-management (ATM) system. The TBO operation concept takes into account the trajectory of every aircraft during all phases of the flight and manages their interactions to achieve the optimum system outcome, with minimal deviation from the user requested flight trajectry, whenever possbile. However, as TBO is based on a constant exchange of information about trajectories between the ground and air systems, uncertainties inherent in the ATM system sometimes lead to a degradation of its performance when disruptions occur. The EU-funded START project aims to design, apply and verify optimised algorithms that will enable a robust ATM system not only for conventional air traffic but resilient in disrupted circumstances as well

Simulation Exercises for robust Flight dispatching solution under thunderstorm disruptions

The development, implementation and validation of optimisation algorithms for robust airline operations that result in stable and resilient Air Traffic Management (ATM) performance even in disturbed scenarios are the overall goals of START. This presentation focusses on the validation part. The validation of the START robust airline operations is performed by comparing the performance of a reference and a resilient scenario under disturbed and undisturbed conditions. The reference scenario is derived from the traffic demand for two days in 2018, June 7th and June 10th with strong convective weather phenomena. The resilient scenario is built on the reference scenario but is prepared for more frequent planning updates due to changing forecasts of capacity shortfalls mainly caused by weather impacts. Resiliency refers to the intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances. Within the validation trials performed, disturbances are included by means of convective weather areas which are handled as No-Fly-Zones (NFZ). Validation of the START results is performed threefold. First, reference and resilient scenarios are compared, mainly focussing on expected duration of overall conflict hours of aircraft with other aircraft and convective weather zones. Second, real life departure uncertainties are added by means of Monte-Carlo simulations with different distributions. Finally, scenarios are resolved with conflict resolution algorithms above FL150 as far as possible. The presentation gives an overview of the validation results, showing an overall low but stable benefit for the adapted aircraft fleet (Star Alliance) of the resilient scenario, with no negative effects for the global scenario

A volcanic-hazard demonstration exercise to assess and mitigate the impacts of volcanic ash clouds on civil and military aviation

International audienceAbstract. Volcanic eruptions comprise an important airborne hazard for aviation. Although significant events are rare, e.g. compared to the threat of thunderstorms, they have a very high impact. The current state of tools and abilities to mitigate aviation hazards associated with an assumed volcanic cloud was tested within an international demonstration exercise. Experts in the field assembled at the Schwarzenberg barracks in Salzburg, Austria, in order to simulate the sequence of procedures for the volcanic case scenario of an artificial eruption of the Etna volcano in Italy. The scope of the exercise ranged from the detection (based on artificial observations) of the assumed event to the issuance of early warnings. Volcanic-emission-concentration charts were generated applying modern ensemble techniques. The exercise products provided an important basis for decision-making for aviation traffic management during a volcanic-eruption crisis. By integrating the available wealth of data, observations and modelling results directly into widely used flight-planning software, it was demonstrated that route optimization measures could be implemented effectively. With timely and rather precise warnings available, the new tools and processes tested during the exercise demonstrated vividly that a vast majority of flights could be conducted despite a volcanic plume being widely dispersed within a high-traffic airspace over Europe. The resulting number of flight cancellations was minimal

EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds

The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986, last access: 5 November 2021). The alert products developed by the EUNADICS-AV EWS, i.e. near-real-time (NRT) observations, email notifications and netCDF (Network Common Data Form) alert data products (called NCAP files), have shown significant interest in using selective detection of natural airborne hazards from polar-orbiting satellites. The combination of several sensors inside a single global system demonstrates the advantage of using a triggered approach to obtain selective detection from observations, which cannot initially discriminate the different aerosol types. Satellite products from hyperspectral ultraviolet-visible (UV-vis) and infrared (IR) sensors (e.g. TROPOMI - TROPOspheric Monitoring Instrument - and IASI - Infrared Atmospheric Sounding Interferometer) and a broadband geostationary imager (Spinning Enhanced Visible and InfraRed Imager; SEVIRI) and retrievals from ground-based networks (e.g. EARLINET - European Aerosol Research Lidar Network, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e.g. selective ash detection, SO2 column and plume height, dust cloud, and smoke from wildfires). A total of 23 different alert products are implemented, using 1 geostationary and 13 polar-orbiting satellite platforms, 3 external existing service, and 2 EU and 2 regional ground-based networks. This allows for the identification and the tracking of extreme events. The EUNADICS-AV EWS has also shown the need to implement a future relay of radiological data (gamma dose rate and radionuclides concentrations in ground-level air) in the case of a nuclear accident. This highlights the interest of operating early warnings with the use of a homogenised dataset. For the four types of airborne hazard, the EUNADICS-AV EWS has demonstrated its capability to provide NRT alert data products to trigger data assimilation and dispersion modelling providing forecasts and inverse modelling for source term estimate. Not all of our alert data products (NCAP files) are publicly disseminated. Access to our alert products is currently restricted to key users (i.e. Volcanic Ash Advisory Centres, national meteorological services, the World Meteorological Organization, governments, volcano observatories and research collaborators), as these are considered pre-decisional products. On the other hand, thanks to the EUNADICS-AV-SACS (Support to Aviation Control Service) web interface (https://sacs.aeronomie.be, last access: 5 November 2021), the main part of the satellite observations used by the EUNADICS-AV EWS is shown in NRT, with public email notification of volcanic emission and delivery of tailored images and NCAP files. All of the ATM stakeholders (e.g. pilots, airlines and passengers) can access these alert products through this free channel

EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds

Abstract. The purpose of the EUNADICS-AV (European Natural Airborne DisasterInformation and Coordination System for Aviation) prototype early warningsystem (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986, last access: 5 November 2021). The alert products developed by the EUNADICS-AV EWS, i.e. near-real-time (NRT) observations, email notifications and netCDF (Network Common Data Form) alert data products (called NCAP files), have shown significant interest in using selective detection of natural airborne hazards from polar-orbiting satellites. The combination of several sensors inside a single global system demonstrates the advantage of using a triggered approach to obtain selective detection from observations, which cannot initially discriminate the different aerosol types. Satellite products from hyperspectral ultraviolet–visible (UV–vis) and infrared (IR) sensors (e.g. TROPOMI – TROPOspheric Monitoring Instrument – and IASI – Infrared Atmospheric Sounding Interferometer) and a broadband geostationary imager (Spinning Enhanced Visible and InfraRed Imager; SEVIRI) and retrievals from ground-based networks (e.g. EARLINET – European Aerosol Research Lidar Network, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e.g. selective ash detection, SO2 column and plume height, dust cloud, and smoke from wildfires). A total of 23 different alert products are implemented, using 1 geostationary and 13 polar-orbiting satellite platforms, 3 external existing service, and 2 EU and 2 regional ground-based networks. This allows for the identification and the tracking of extreme events. The EUNADICS-AV EWS has also shown the need to implement a future relay of radiological data (gamma dose rate and radionuclides concentrations in ground-level air) in the case of a nuclear accident. This highlights the interest of operating early warnings with the use of a homogenised dataset. For the four types of airborne hazard, the EUNADICS-AV EWS has demonstrated its capability to provide NRT alert data products to trigger data assimilation and dispersion modelling providing forecasts and inverse modelling for source term estimate. Not all of our alert data products (NCAP files) are publicly disseminated. Access to our alert products is currently restricted to key users (i.e. Volcanic Ash Advisory Centres, national meteorological services, the World Meteorological Organization, governments, volcano observatories and research collaborators), as these are considered pre-decisional products. On the other hand, thanks to the EUNADICS-AV–SACS (Support to Aviation Control Service) web interface (https://sacs.aeronomie.be, last access: 5 November 2021), the main part of the satellite observations used by the EUNADICS-AV EWS is shown in NRT, with public email notification of volcanic emission and delivery of tailored images and NCAP files. All of the ATM stakeholders (e.g. pilots, airlines and passengers) can access these alert products through this free channel.info:eu-repo/semantics/publishe