As of September 2022, flight numbers in Europe have returned to 88% of the levels seen prior to the global outbreak of Covid-19, and major European hubs such as London Heathrow are again processing more than 1000 runway movements (i.e. landings or take-offs) per day on average. Large volumes of air traffic impose heavy demands on airport infrastructure, with runway capacity being the most critical bottleneck. Demand-capacity imbalances result in flight delays, which not only disrupt airline and passenger itineraries but also have serious financial consequences and environmental impacts. In order to mitigate the risk of flight delays, various types of interventions are possible. "Strategic" interventions are those that are made far in advance of a particular day of operations, before any 'real-time' information (e.g. weather conditions, airline crew shortages) becomes known. These types of interventions typically involve restricting the numbers of arrivals and departures that can be scheduled per hour at an airport. On the other hand, "tactical" interventions are those that are made on a particular day of operations in response to events that unfold in real time. For example, air traffic controllers have knowledge of the latest positions and estimated arrival times of aircraft that are due to arrive in the terminal airspace and can use this information to plan the most efficient sequence of aircraft landings in order to maximise runway throughput rates and reduce expected airborne holding times. In current practice, airport scheduling is carried out via a process known as "slot coordination". Airport schedules are required to comply with airport capacity declarations, which impose limits on hourly numbers of scheduled runway movements. However, even if an airport's schedule is consistent with its capacity declaration, there is no guarantee that the delays seen under that schedule will remain within `acceptable' limits - as, in reality, these delays depend on a range of stochastic factors (e.g. upstream delays, weather conditions) as well as the real-time tactical interventions implemented by air traffic controllers. We propose to develop a new framework for airport schedule optimisation which explicitly models airport delays through a high-fidelity, stochastic and dynamic model of air traffic control and aims to ensure that the final airport schedule results in a relatively low risk of delays exceeding 'acceptable' levels. To elaborate further, our proposed optimisation framework consists of two separate (but related) modules: 1. First, we use a mixed integer linear programming (MILP) model to minimise schedule displacement, which is defined as the total amount of deviation between an airport schedule and an ideal 'baseline' scenario. This MILP formulation includes constraints that restrict the numbers of arrivals and departures that can be scheduled in different time slots. 2. The optimal schedule given by the MILP in Step 1 is regarded as a 'candidate' for the final airport schedule. In this step we use a stochastic, dynamic model of the airport sequencing problem to test whether or not the expected delays under the candidate schedule satisfy a set of delay-based performance criteria, which includes components based on punctuality and fuel emissions. This is a tactical optimisation problem in which aircraft sequencing decisions are made under continuously-evolving random conditions. If the performance criteria are satisfied, then the candidate schedule is accepted as the final schedule and the process is completed. Otherwise, we return to Step 1 and reformulate the constraints of the MILP, making them 'tighter' in order to further restrict the numbers of flights that can be scheduled in particular time slots. This process is repeated iteratively (reformulating the MILP constraints as many times as necessary) until a candidate schedule is found which satisfies the delay-based criteria.

The upper troposphere in mid-latitudes is the region encompassing altitudes of around 8-12 km, which includes the jet streams, regions of very strong winds, which are closely related to strengths and paths of the mid-latitude depressions. Climate change is expected to change the nature of the upper troposphere at mid-latitudes - climate models indicate that over coming decades, it will warm, the relative humidity will increase and the strength and orientation of the jet stream might change, and the boundary between the troposphere and the overlying stratosphere (the tropopause) will increase in altitude. However, when different climate models are used to predict future climate change, there is a significant spread in the results they produce; the reasons for this spread are not fully understood. Understanding climate change in the mid-latitude upper troposphere is of importance in its own right, but it has a wider economic significance. The cruise altititude of commercial aircraft is in the upper troposphere and flight times can be strongly affected by the wind conditions. Most obviously, the duration of flights between (as an example) London and New York are normally more than an hour faster when going eastbound, as the aircraft attempt to fly in the jet stream and receive an extra "push" - by contrast, westbound flights normally try to avoid the jet stream as this would impede progress. However, day-to-day variations in weather conditions in the north Atlantic mean that flight durations of both eastbound and westbound flights can vary by up to 100 minutes, depending largely on the strength and position of the jet stream. Since fuel use, and hence carbon dioxide emissions, are closely related to the flight duration, there are both economic and climate consequences for this variation. In our recent research we have shown that the weather in the upper troposphere in the North Atlantic can be split into characeristic patterns (5 in winter and 3 in summer) for which the aircraft routes are distinct. In addition we have shown that other climate effects of aircraft emissions (for example, contrails and ozone change resulting from emissions of oxides of nitrogen) very likely vary between these weather patterns. Since aircraft routing is dependent on the weather situation in the upper troposphere, it is natural to ask whether climate change could impact on aircraft routing. There has been much research on the effect of aviation on climate change, but surprisingly little that asks the reverse question: what is the effect of climate change on aviation? Our proposal aims to answer this question, while at the same time improving understanding of upper tropospheric climate change. Since the aviation industry aims to put constraints on its carbon dioxide emissions, the effect of climate change on aviation routing could either assist or work against these aims. We will consider how the routes of individual aircraft may be affected by the changes in the frequency of different weather patterns in the North Atlantic, predicted by a number of different climate models. We will exploit a recent, large and easily available set of simulations of possible future climate change from a range of world-leading climate models that have been produced for the fifth assessment report of the Intergovernmental Panel on Climate Change, which is currently being written. We will assess how well the climate models reproduce the present-day weather patterns in the North Atlantic and then look at how these patterns change for various possible future climates. We will then see how aircraft routing is affected by these weather patterns and compute the impact of this carbon dioxide emissions. We will also investigate the impact of both the climate change and re-routing on the other climate impacts of aviation. We will extend this work to cover the North Pacific, which is expected to show a significant increase in air traffic over coming decades.

The ambition of this partnership between NATS and The Alan Turing Institute is to develop the fundamental science to deliver the world's first AI system to control a section of airspace in live trials. Our research will take a hierarchical approach to air traffic control (ATC) by developing a digital twin alongside a multi-agent machine-learning control system for UK airspace. Furthermore, the partnership will develop technical approaches to deploy trustworthy AI systems, considering how safety, explainability and ethics are embedded within our methods, so that we can deliver new tools which work in harmony with human air traffic controllers in a safety-critical environment. Little has changed in the fundamental infrastructure of UK airspace in the past 50 years, but demand for aviation has increased a hundredfold. Aviation 2050, a recent government green paper, underlines the importance of the aviation network to the prosperity of the UK to the value of £22 billion annually. Yet our nation is at risk without rapid action to modernise our airspace and control methods, to ensure they can handle a future increase in UK passenger traffic of over 50% by 2050 and new challenges arising from unmanned aircraft, both against a backdrop of increasing global pressures to transform the sector's environmental impact. The augmentation of live air traffic control through the use of AI agents which can handle the complexity and uncertainties in the system has transformative potential for NATS's business. This will positively impact live operations, as well as a research tool and training facility for new ATCOs. Correspondingly, NATS's research vision is to exploit new approaches to AI that enable increases in safety, capacity and environmental sustainability while streamlining air traffic controller training. The anticipated benefits of AI systems to air traffic control have come at a critical time, providing us with an opportunity to respond effectively to the unprecedented challenges which arise from a triad of crises: the coronavirus 2019 (Covid-19) pandemic, Brexit and global warming. The UK must develop independent technical advances in the sector, without compromising sustainability targets. The Alan Turing Institute is positioned at the rapidly evolving frontiers of probabilistic machine learning, safe and trustworthy AI and reproducible software engineering. Matching this with the world-leading expertise of NATS, supported by a world-first data set of more than 20 million flight records, means that this partnership is in a unique position to build the first multi AI agents system to deliver tactical control of UK airspace.

Resilience is a vital property of communications systems and unified ICT environments, and is achieved mainly by infrastructural redundancy, and static security and network control (e.g., through multipath routing protocols, signature-based intrusion detection systems). This results in mostly monolithic solutions that are service and location-specific, and they protect the infrastructure against a static and well-defined set of threats. However, current approaches do not incorporate, nor do they take advantage of, the wealth of spatio-temporal information available in today's ICT environments, such as sensing, logs, packet data, or external global media feeds. Such diverse data and information sources from heterogeneous environments unified over ICT infrastructures can be exploited to create situation awareness, and can help protect the infrastructure from a range of dynamic and emerging adversarial events (e.g., from new types of failures due to complexity and centralisation, to denial of service attacks and natural disasters) that current static approaches fail to provide [1][2][3]. At the same time, today's ICT environments are evolving as crucial, mission-critical socio-economic systems, and their optimal performance depends on adaptive and intelligent schemes to ensure resilient operation at the onset of legitimate or malicious adversarial events. In order to realise this aim, there needs to be a suitable instrumentation, measurement, analysis, and control infrastructure that will operate natively with, and add intelligence to, the unified networked environment. In this project, we propose to design and develop a generic, resilient and adaptive situation-aware information infrastructure that would predict and confront the broad range of challenges faced by the network. We aim to provide novel and practical mechanisms that will enable a deeper understanding of the dynamic and non-stationary evolution of mission-critical systems through harnessing 'big data' sets of relevant internal (monitored) and external (global media feeds) spatio-temporal information - what we call 'context'. Our mechanisms will be incorporated as a protocol suite within a Software-Defined architecture, integrated as a native component in (future) computer networks design. This project is not simply aiming at integrating off-the-shelf solutions into a unified scheme, but rather to revisit the resilience challenge in mission-critical ICT environments and contribute new solutions to the information processing, algorithmic, networking and systems aspects of such undertakings. The research will be carried out over two years jointly at the Universities of Lancaster and Glasgow, involving investigators with a wide range of expertise (from resilient and autonomic communications, through network instrumentation and management, to information retrieval) and in collaboration with a number of leading industrial partners in the areas of safety-critical systems (NATS), industrial control networks (EADS-IW), and hardware-accelerated custom computation products (Solarflare). This consortium will ensure delivery of excellent research results with direct industrial applicability and transformative effects on future intelligent mission-critical infrastructures. [1]. Windows Azure service interruption: http://blogs.msdn.com/b/windowsazure/archive/2012/08/02/root-cause-analysis-for-recent-windows-azure-service-interruption-in-western-europe.aspx [2]. Air Traffic Management system malfunction at Dublin Airport: http://www.computerworld.com/s/article/9110319/Dublin_Airport_radar_system_brought_down_by_faulty_network_card [3]. Power outage hits London Data Centre: http://www.theregister.co.uk/2012/07/10/data_centre_power_cut/

Turbulence is the leading cause of weather-related aircraft incidents and the underlying cause of many people's fear of air travel. One estimate of turbulence indicates over 63,000 encounters with moderate-or-greater turbulence and 5000 encounters with severe-or-greater turbulence annually. In 34 years, the US reported 883 fatalities associated with turbulence. Turbulence can also damage aircraft, by tearing off winds and engines, as happened in an extreme turbulent event over Colorado in 1992. The economic costs of turbulence are more than just injuries and damage, with flight delays, inspections, repairs, and post-accident investigations also taking their toll. Estimates of the total cost to US carriers alone are nearly $200 million annually. Although the costs of turbulence to UK/EU airlines and over EU airspace are not available, assuming the occurrence of turbulence and the density of air travel are similar to that over the US and that the EU is about the same size as the US, then costs should be comparable. Moreover, climate change is exacerbating the problem. Midlatitude turbulence diagnosed from climate projections increase under increasing atmospheric carbon dioxide, with a doubling or trebling later this century. Thus, the costs of turbulence due to climate change will lead to a substantial increase in turbulent events. Clear-air turbulence, abbreviated as CAT, is turbulence that occurs away from clouds in clear air. CAT is difficult for pilots to detect and for forecasters to predict. One of the reasons that it is difficult to predict is that CAT is believed to have multiple sources and no single forecasting tool works for all of the sources. One suspected source of CAT is the release of hydrodynamic instability, an imbalance between different forces in the atmosphere that lead to large and rapid accelerations of the air. Such accelerations may produce atmospheric phenomena such as roll-type circulations or wave-like motions that result in CAT. Presently, we have an incomplete understanding of how hydrodynamic instability forms, releases, produces turbulence, and returns to stability. In this proposed research, we will look at observations of turbulence from three sources. One is from a vertically pointing radar in Wales that can detect turbulence at the jet stream. A second one is from pilots manually reporting turbulence. A third is from automated instrumentation aboard aircraft. We will use these observations to understand the conditions in which CAT forms and its relationship to hydrodynamic instability. Because these observations are snapshots in time from single measurements, computer model simulations of real and idealised weather phenomena that produce CAT will be critical to determine how the instability forms, how the instability and resulting turbulence evolves, and how the atmosphere returns to balance after the release of the instability. Within the context of the results from the observations, we will construct the life cycle of CAT from its origin, to its growth, to its demise. Given these new insights, we will develop tools for model output (called diagnostics) to quantify the impacts from the release of the instability and evaluate the performance of these diagnostics over North America, the North Atlantic Ocean, and Europe. In this way, improved understanding of the CAT life cycle will lead to better predictions of jet-stream turbulence, as well as reduced costs and injuries to passengers and flight crew.