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

Vista D6.3 - Stakeholder consultation on initial assessment

This deliverable contains the summary of consultation activities carried out by Vista to validate and obtain feedback on the first results obtained from the model. Consultation activities have been conducted in different forums (workshop/conferences) and with a dedicated consultation to targeted experts and stakeholders. The deliverable contains the main findings from these consultation activities and the next steps to finalise the development of Vista’s model and the production of the final results considering the feedback obtained

The S2N2 metallicity calibrator and the abundance gradient of M 33

We introduce the log(Ha/[SII]6717+6731) vs. log(Ha/[NII]6583) (S2N2) diagnostic diagram as metallicity and ionisation parameter indicator for HII regions in external galaxies. The location of HII regions in the S2N2 diagram was studied both empirically and theoretically. We found that, for a wide range of metallicities, the S2N2 diagram gives single valued results in the metallicity-ionisation parameter plane. We demonstrate that the S2N2 diagram is a powerful tool to estimate metallicities of high-redshift (z ~ 2) HII galaxies. Finally, we derive the metallicity for 76 HII regions in M33 from the S2N2 diagram and calculate an O/H abundance gradient for this galaxy of -0.05 (+-0.01) dex kpc^-1.Comment: 10 pages, 3 figures and 2 tables. Accepted for publication in MNRA

Pilot3 crew multi-criteria decision support tool estimating performance indicators and uncertainty

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

Vista D1.2 - Final Project Results Report

Vista assesses primary trade-offs in ATM between key performance areas in the current and future timeframes, including how they are affected by SESAR and non-SESAR factors. The project has examined the effects of market forces (e.g. fuel prices, economic development), technologies and regulatory factors on European performance in ATM, through the evaluation of stakeholder and environmental indicators. The approaches selected for the various layers in the model are described, and the corresponding results of each are presented. The strategic layer implements an agent-based economic model and a schedule mapper to generate demand and capacity for the different stakeholders, flight schedules and passenger flows – it feeds the pre-tactical layer. The pre-tactical layer transforms the output of the strategic layer into individual flight plans, passenger itineraries and ATFM regulations delay – it feeds the tactical layer. The tactical layer runs the day of operations, tracking flights and passengers and reacting to the tactical situation in the system. Trade-offs have been assessed and visualised within and between periods, and between stakeholders. The Final Project Results Report provides a summary of the entire project, from objectives to main conclusions. Lessons learned and recommendations for future research and development activities are reported along with a self-assessment of the project’s maturity

Domino D6.2 - Stakeholders' consultation on system and investigative case studies

For completeness, this deliverable presents the consultation questionnaire and a summary of the consultation results on the system architecture and the investigative case studies to be modelled in Domino: the feedback has already been incorporated into deliverables D3.1 (Architecture definition) and D3.2 (Investigative case studies description)

Domino D6.3 - Workshop results summary

This deliverable summarises two workshop activities carried out with stakeholders to provide feedback on the modelling, metrics and first results of Domino. How the feedback will be used in the project is highlighted

Estimating economic severity of Air Traffic Flow Management regulations

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