Efficiency versus Resilience

Pablo Hernández

2022-12-13 14:22:32
Reading Time: 5 minutes

How Predictive Risk Information Could Influence the Trade-off in ATM

The next generation Air Traffic Management (ATM) systems are pushed more and more towards digitization, fuelled by the demands to increase capacity and cost-efficiency while also increasing the already high safety and resilience levels. SafeOPS investigates an AI-based decision support tool, which provides Air Traffic Controllers with predictive risk information on the likelihood of approaches to perform a go-around, in real time. The underlying idea is to enable Air Traffic Controllers to incorporate predictive analytics in pre-planning during departure and approach handling, improving the decision-making in terms of capacity as well as safety and resilience.

Resilience is defined differently in various contexts. From Oxford Advanced Learner’s Dictionary [1], one intuitive definition in a personal and human context  is “the ability of people (…) to feel better quickly after something unpleasant, such as shock, injury, etc. happened”. More in general, resilience describes the ability to absorb, adapt or recover from rare or unpredictable events and disturbances. This definition is not limited to humans, but can also be applied to systems, organizations or operations.

Disruptions affect a system or organisation in normal operation. They cause discontinuity, confusion, disorder or displacement and can take various forms. Madni and Jackson [2] categorised major disruptions as originating from operational contingencies, natural disasters, political instability or economic events. Resilient systems are able to react and adapt to these disruptions. While it is possible to protect a system against known or foreseeable events by analysing historical data/events, it is much harder to anticipate and counter rare or even completely unknown disruptions. This implies that systems need adequate safety margins to cope with uncertainty [2, p.2]. Madni and Jackson [2, p.3] conclude that humans are more capable of detecting and handling unpredicted situations than machines. Thus, humans are considered as net assets for resilience in systems like Air Traffic Management.

As of today, Air Traffic Controllers recognise the onset of go-arounds through pilots’ communication, from observing the aircraft visually or via radar. Go-arounds are a standard and well-established flight procedure, for flight crews as well as for Air Traffic Controllers . Within the states of ECAC, they occur with an average rate of 1-3 per 1000 approaches [3]. Despite the relatively low likelihood of a go-around to occur, Air Traffic Controllers have strategies for handling them. These strategies are of reactive nature, meaning they become active once the Air traffic Controller identifies the ongoing go-around. But especially in high traffic congestions, the Controller might realize that a go-around is ongoing, after a departure has been cleared for take-off or is airborne already. In these situations, handling a go-around becomes complex, since knock-on effects like separation or wake turbulence challenges with preceding departures can arise. Air Traffic Controllers are trained for such situations, nevertheless, resolving such situations, while maintaining safety, drastically increase the workload of the Air Traffic Controller, as well as the Flight Crews.

These situations serve as good examples for resilience in the ATM system and the role of human within. It has to be emphasized that the described go-around scenarios, including traffic congestion and a possible conflict between missed approach and departure routes, are only a subset of all go-around scenarios. Especially in times of medium to low traffic volume, pilots most likely can fly briefed standard missed approach procedures without any safety relevant knock on effects occurring. In these cases, the existing procedures have proven and will continue to prove adequate safety barriers with Air Traffic Controllers providing the necessary resilience in the Air Traffic Management System.

However, fueled by Covid-19, a general debate on a “Resilience versus Efficiency” trade-off emerged [4]. In [5], Moshe Y. Vardi transfers the lessons learned from the Covid-19 pandemic to the computing domain (The content of the article is also available as online lecture on youtube). Thereby he partly pounces on the opinion article of William Galston in the [6], which describes the Covid-19 related impact on US businesses, followed-up with the question whether this domain, streamlined towards efficiency and thus lacking the adaptive capacity (required for resilience), has become vulnerable to disruptions. Also supply chains, which were deemed efficient but identified to lack resilience during Covid-19 [7], are now heavily subsidized in the US, to ensure better energy security and more independence from foreign and competitive countries [8].

This efficiency vs. resilience trade-off is also relevant for Air Traffic Management. The European Air Traffic Management Master Plan’s ambitions envisions a 60% increase in network throughput of IFR flights by 2035, compared to 2012, and an increase of 5%-10% at congested airports [9]. Simultaneously the ambitions include an increase in safety levels by 100%. Following the “Resilience versus Efficiency” debate, these two goals could prove challenging to conciliate. The first contributing factor for complex go-around situations, the conflicting procedures, roots in environmental conditions around an airport, like terrain or noise abatement restrictions, and is independent from the stated ambitions. High traffic situations in the ATM network, however, will increase, also making the scenario for complex go-arounds more frequent, which is the second contributing factor. Therefore, complex go-around situations serve as good scenarios to investigate, how decision intelligence could provide a benefit for safety and resilience in Air Traffic Management.

Decision intelligence is an engineering discipline providing a framework which incorporates (predictive) data science in decision-making processes [10]. It aims to support, augment or automate decisions, by providing additional insights gained from data, which were not available in the prior decision-making process. SafeOPS, used the decision intelligence framework to investigate the decision-making processes of Air Traffic Controllers under the premise that data-driven go-around predictions are provided to them. The idea when providing predictive risk information in this scenario is, that Air Traffic Controllers are enabled to use proactive tactics instead of the reactive tactics described above, to avoid the described knock on effects possibly triggered by a go-around. SafeOPS found that these proactive solutions benefits the safety and resilience of the ATM system in complex go-around situations, by providing the ATCOs with more time and better information for the necessary coordinative actions, which have to be taken in the event of a go-around. On the contrary, the proactive tactics can reduce capacity/efficiency, e.g. in case of false prediction, but only in an amout, which is negligable, compared to the forseen overall increase of capacity. Thus, predictive risk information can be used as a decision support between a reacitve and proactive approach to handle go-arounds, especially in high traffic situations.

All technical deliverables and publications, documenting the approach and results of the project are available under https://safeops.eu/#safeopsdeliverables

SafeOPS has received funding from the SESAR Joint Undertaking (JU) under grant agreement No 892919. The JU receives support from the European Union’s Horizon 2020 research and innovation program and the SESAR JU members other than the Union.


[1] S. Wehmeier und A. S. Hornby, Hrsg., Oxford advanced learner’s dictionary of current English, 6. ed., [Nachdr.] Hrsg., Oxford: Oxford Univ. Press, 2004, p. 1539.

[2] A. M. Madni und S. Jackson, „Towards a Conceptual Framework for Resilience Engineering,“ IEEE Systems Journal, Bd. 3, Nr. 2, p. 181–191, 2009.

[3] T. B. Blajev and W. Capt. Curtis, “Go-around decision-makingand execution project: Final report to flight safety foundation,” 2017, online: https://flightsafety.org/wp-content/uploads/2017/03/Go-around-study_final.pdf, accessed: 12. November 2022.

[4] T. L. Friedman, “How we broke the world: Greed and globalization set us up for disaster.” New York Times, p. Page 4, 31 May 2020, online: https://www.nytimes.com/2020/05/30/opinion/sunday/coronavirus-globalization.html, accessed: 31. October 2022.

[5] M. Y. Vardi, “Efficiency vs. resilience: What covid-19 teaches computing,” Communications of the ACM, vol. 63, no. 5, p. 9, 2020, online: https://cacm.acm.org/magazines/2020/5/244316-efficiency-vs-resilience/fulltext, accessed: 31. October 2022.

[6] W. A. Galston, “Efficiency isn’t the only economic virtue: It often comes at the expense of resilience, as the new coronavirus is making clear.” Wall Street Journal, 10 March 2020, online: https://www.wsj.com/articles/efficiency-isnt-the-only-economic-virtue-11583873155?mod=saved_content, accessed: 31. October 2022.

[7] W. A. Reinsch, “Resilience vs. Efficiency” Center for Strategic & International Studies, 14 June 2021, online: https://www.csis.org/analysis/resilience-vs-efficiency, accessed 24. November 2022.

[8] P. Dvorak, “The U.S.’s Struggle to Wean Itself From Chinese Solar Power” Wall Street Journal, 15. November 2022, online: https://www.wsj.com/articles/solar-energy-china-supply-chain-11668525614?mod=saved_content, accessed 24. November 2022.

[9] SESAR Joint Undertaking, European ATM master plan: Digitalising Europe’s aviation infrastructure, 2020th ed. Publications Office of the European Union, ISBN: 978-92-9216-134-7,  online: https://www.atmmasterplan.eu/, accessed: 31.October 2022.

[10] C. Kozyrkov, “Introduction to Decision Intelligence,” 2019, online: https://towardsdatascience.com/introduction-to-decision-intelligence-5d147ddab767, accessed: 24. November 2022.

Co-authored by Pablo Hernández and Lukas Beller

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