EUROCONTROL data analytics are driving a bright future
A variety of studies have recently forecast continued long-term growth in traffic demand, with resulting increases in delays and constrained access to airports. The cost to the European economy and impact on the travelling public is forecast to be billions of euros.
Operational improvements under development in the European ATM Research Programme, SESAR, will bring some respite. However, researchers are turning to the power of big data, artificial intelligence and the sub-domain machine learning to boost efficiency and operational performance. Data-related improvements on runway throughput, for example, can result in five to 10 per cent additional capacity at peak traffic times for a relatively affordable investment.
The application of machine learning (ML) is one area being vigorously researched. ML provides a system with the ability to learn by looking for patterns in data that can result in accurate predictions.
Data-driven predictions of future events will support smart strategies to manage passenger and aircraft flows, such as passengers moving through airport terminals, boarding aircraft and air traffic arrival sequence predictability – they will help pilots and air traffic controllers to focus on priority tasks. This is very dependent on achieving optimal human/machine teaming, and being able to easily explain the ML application.
However, ATM remains a safety-critical industry, and data analytics applications require rigorous research to ensure that they are capable of meeting the high safety and security requirements of aviation. Verifying and validating ML-related operational improvements in the live ATC environment remains a significant challenge for regulators. Nevertheless, we must not forget performance benefits can be had from non-safety critical applications.
Recognising the increasing opportunity of data in predictive applications, EUROCONTROL – together with stakeholders in areas such as airspace, network management and runway management techniques – is actively investigating the use of data analytics to identify performance improvements and benefits.
Some early applications investigated the predictability of aircraft turnaround at the gate and taxi-out time (TXOT) in support of performance prediction tools to be used in an Airport Operations Centre (APOC). The expectation is that, with access to accurate data, decision-making is improved and operational staff become more proactive.
One application tested produced TXOT predictions using data from Paris-Charles de Gaulle, which improved the prediction robustness compared to traditional statistical analyses. Furthermore, TXOT predictions were quickly computed in a few minutes.
Another application, developed in the EU SafeClouds project, predicted the runway exit to be used after landing during high intensity runway operations in order to help tower controllers judge separation minima between leader and follower aircraft, thus optimising runway occupancy time and reducing potential go-around or missed approaches.
In the runway exit application, a prediction is produced at 2NM from the runway threshold and presented to the controller through a runway tactical support tool. This tool advises the controller of the predicted arrival runway occupancy time (AROT) and runway exit for each aircraft, helping the controller to anticipate any separation reduction for the following arriving aircraft.
The ML runway exit support tool was based on Vienna airport and tested in a SESAR Real Time Simulation undertaken by EUROCONTROL. Vienna controller Philipp Wächter, who took part in the simulation, said that the “controller support tool for AROT and runway exit prediction was considered operationally feasible and acceptable”.
Whilst this work will be continued to pre-implementation level in SESAR, a real-time prototype was later developed and tested with data from Orly Airport, with critical wind data provided by a ‘Leosphere’ Lidar to improve prediction accuracy.
