Periodic Reporting for period 2 - START (a Stable and resilient ATM by integrating Robust airline operations into the network)
Reporting period: 2021-05-01 to 2022-10-31
One of the key enablers of Trajectory Based Operations (TBO) is the automated updating of trajectories in reaction to developing uncertainties. However, a high frequency of updates and modifications leads to degraded system stability.
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).
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).
At the end of this 2nd reporting period, the overall achievement can be set at 100%. Out of the 5 objectives described above, all of them have been successfully achieved.
We have been able to answer positively the Research questions posed at the beginning of the project
Research Questions (RQ)
RQ#1: Can trajectory level uncertainty be modelled, assimilated on a cycle based, and propagated?
RQ#2: Can ATM network uncertainty be modelled (including thunderstorms), cyclically assimilated, and propagated?
RQ#3: Can a robust operational plan for ATM system resilience be found?
RQ#4: Can this advanced functionality (build upon successful achievement of RQ#1 to RQ#3) be implemented in operational dispatching tools such as FLIGHTKEYS’s one?
Conclusion #1: The proposed study has the applicability and suitability for uncertainty propagation in the aircraft trajectory prediction process. It has been shows how, when applying the framework to a relevant scenario within the European air traffic, the results obtained for estimating the probability distribution of the flight times resembles the actual values observed, even when considering weather uncertainties and thunderstorms as disruptive events. It obtains results comparable to those retrieved by using a complex aircraft trajectory prediction tool while reducing the involved computational time and complexity, as it avoids the application of computationally demanding methods (e.g. a Monte Carlo simulation).
Conclusion #2: All in all, the results prove that ATM network uncertainty can be modeled and propagated. We have observed that regarding the problems of modeling network propagations, a combination of mathematical model and data-driven approach provides more flexibility and robustness compared to a fully switch over from mathematical modeling to purely data-driven approach. This way it becomes possible to fully harness both the stability of mathematical models and the insights of data-driven models.
Conclusion #3: The results show that a robust operational plan can be obtained for a set of flights or for the complete set of flights.
The answer to RQ#4 is affirmative:
Conclusion #4: All in all, we have shown that all the methodologies, processes, and algorithms developed within START project could be integrated in a flight dispatching tool such as FK5D. It would, of course, require further development and integration, but this can be considered as future research and development.
We have been able to answer positively the Research questions posed at the beginning of the project
Research Questions (RQ)
RQ#1: Can trajectory level uncertainty be modelled, assimilated on a cycle based, and propagated?
RQ#2: Can ATM network uncertainty be modelled (including thunderstorms), cyclically assimilated, and propagated?
RQ#3: Can a robust operational plan for ATM system resilience be found?
RQ#4: Can this advanced functionality (build upon successful achievement of RQ#1 to RQ#3) be implemented in operational dispatching tools such as FLIGHTKEYS’s one?
Conclusion #1: The proposed study has the applicability and suitability for uncertainty propagation in the aircraft trajectory prediction process. It has been shows how, when applying the framework to a relevant scenario within the European air traffic, the results obtained for estimating the probability distribution of the flight times resembles the actual values observed, even when considering weather uncertainties and thunderstorms as disruptive events. It obtains results comparable to those retrieved by using a complex aircraft trajectory prediction tool while reducing the involved computational time and complexity, as it avoids the application of computationally demanding methods (e.g. a Monte Carlo simulation).
Conclusion #2: All in all, the results prove that ATM network uncertainty can be modeled and propagated. We have observed that regarding the problems of modeling network propagations, a combination of mathematical model and data-driven approach provides more flexibility and robustness compared to a fully switch over from mathematical modeling to purely data-driven approach. This way it becomes possible to fully harness both the stability of mathematical models and the insights of data-driven models.
Conclusion #3: The results show that a robust operational plan can be obtained for a set of flights or for the complete set of flights.
The answer to RQ#4 is affirmative:
Conclusion #4: All in all, we have shown that all the methodologies, processes, and algorithms developed within START project could be integrated in a flight dispatching tool such as FK5D. It would, of course, require further development and integration, but this can be considered as future research and development.
START project has developed a holistic comprehensive simulation framework, able to generate stable network-wide optimised robust and resilient solutions to ATM operations in the presence of varying uncertainties, without unduly restricting the solution space (e.g. due to an excessive allowance for uncertainties). This has enabled the determination of a theoretical optimum and the sensitivity of the system to operational and contextual variations and perturbations. It has provided a novel platform to study the optimal trade-off between predictability and flexibility and thereby the levels of stakeholder coordination and automation required to meet the necessary performance requirements, including resilience. The framework focuses on convergence to a stable and robust set of trajectories in the presence of uncertainties by developing a novel understanding of error propagation throughout the system. The project therefore also establishes the key pillar for enabling a study of optimised implementations of long-term operational concepts well beyond SESAR, a key focus for ER
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
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