Periodic Reporting for period 1 - START (a Stable and resilient ATM by integrating Robust airline operations into the network)
Reporting period: 2020-05-01 to 2021-04-30
The overall goal of START is to develop, implement, and validate optimisation algorithms for robust airline operations that result in stable and resilient ATM performance even in disturbed scenarios.
Specific goals include:
Objetive 1: To model uncertainties at the micro (trajectory) level, assimilate observations (via ADSB/Radar) every 15 min and propagate trajectory uncertainties using assimilated models and a stochastic trajectory predictor.
Objetive 2: To model uncertainties at the macro (ATM network) level, assimilate observations (satellite data for storm, and network status) every 15 min., and propagate ATM network uncertainties using the assimilated models.
Objetive 3: To develop an Artificial Intelligence (AI) algorithm capable of generating a set of pan-European (i.e. considering the whole traffic over Europe) robust trajectories that make the European ATM system resilient when facing these relevant validate ties.
Objetive 4: To implement those algorithms as an advanced fight dispatching demo functionality for airspace users to obtain robust trajectories.
Objetive 5: To validate these concepts through system-wide simulation procedures in order to evaluate their stability.
Innovation in START will certainly bring social contributions. The future ATM systems must be built to cope with an already increasing demand (that is expected to double in the medium term), to increase (or at the minimum maintain) the levels of safety, to reduce the cost of service as much of possible (we need to be a competitive business), and all this while also meeting social sensitivities as it is the environment. All these high-level goals respond to social demands. Enabling TBO concept is key towards these goals, and the design of robust and resilient network is in turn key to enable TBO. Thus, society will indirectly benefit from the network-based solution expected as outcome of the project. Overall, it will have a tremendous impact both economically (e.g. jobs, GDP) and socially (e.g. reducing delays).
Out of the 5 objectives described above:
• Objective 1 –WP2- has been fully achieved. An end-to-end framework has been developed in order to propagate the sources of uncertainty at the micro-level affecting the aircraft trajectory prediction process, ranging from the data acquisition and assimilation phase to the generation of probabilistic trajectories for relevant scenarios. A fully time-dependent theoretical posing of the Polynomial Chaos theory was executed, allowing for the propagation of uncertainty along the trajectory and calculation of the output probabilistic trajectories by fitting aPCE polynomials. The sources of uncertainty coming from aircraft intent variables, weather conditions, and initial conditions were identified and implemented in order to allow for their consideration as input variables within the aPCE polynomials. A use case was implemented for a relevant scenario within the European airspace in order to support and show the suitability and applicability of the developed framework for the generation of probabilistic trajectories through the propagation of the uncertainty present at the trajectory-level.
• Objective 2 –WP3- has been fully achieved. The developed 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. 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 and the capability to absorb the uncertainties associated with recovery rates. Uncertainties over individual flight trajectories have been defined through probabilistic distributions where superpose on the arrival times. Moreover, deep learning models have been integrated to capture the nonlinear relationship between the recovery rates, uncertainty accumulation, and disruptive events' attributes. Then, we provide the resiliency definition. From the operational perspective, resiliency has also been associated with the management cost as a function of the intervention rate. The case studies are analyzed for the selected time windows chosen in the interval of 1-10 June of 2018, where thunderstorms affected large areas of North-West Europe.
• Objective 3 –WP4- (AI algorithm for ATM System resilience) and Objective 4 –WP5- (the flight dispatching tool) are both progressing at a good pace (~50%). The independent development (algorithms and system development, respectively) required is almost completed. The remaining of the project will be devoted to the integration of Objective 1 –WP2- and Objective 2 –WP3- outputs to feed the pipeline of the project.
• Objective 5 –WP6- (the assessment of the tool) is also progressing at a good pace, the indicators and the simulation exercises have been conceived. The remaining of the project will be devoted to the execution of the scenarios and its assessment.
START is covering the expected impact indicated in the topic description of the call, “aiming at improving:
● Safety, thanks to better anticipating and managing demand-capacity imbalances;
● Robustness and resilience of the network to perturbations;
● Efficiency thanks to a better monitoring of the Demand and Capacity Balance (DCB) measures and network performance and the implementation of corrective actions;
● Cost-efficiency: an improved network management will allow better planning of ATM resources, as well as making better use of existing capacities, which would lead to reduced ATC and airport costs;
● Flexibility: common awareness to all stakeholders of the network situation and access to opportunities in case of late changes in capacity or demand.”
In particular, START will have an impact by enhancing flexibility of AUs and introducing robustness and resilience in the network. Safety, efficiency and cost-effectiveness are indirect impacts