Blockchain could transform airspace management and flight planning as part of a digital air traffic management future – research and development is under way, exploring the technology and addressing the challenges to its implementation, with a view to unlocking its full potential.

Long recognised as having the ability to transform many aspects of the aviation industry, blockchain technology provides a ledger of digital events that can be replicated, shared and trusted. The block part of the chain is the record or ledger item, which holds the details of the transaction. The transaction is wrapped in a digital signature, which is unique to the block and requires the digital key to decipher. It also includes the digital signature of the previous block, providing a link to earlier transactions so the blocks are strung together in a chain. Existing blocks are unable to be altered in any way, thereby providing a single source of truth.

The Civil Air Navigation Services Organisation (CANSO) has identified ATM applications for blockchain. “In the aviation and ATM domain – that involves a complex system of interconnected resources, processes and applications to deliver the safe and secure services of interwoven system-of-systems, comprised of both human and machine interactions – blockchain applications are several and of key importance,” CANSO said in its report, Emerging Technologies for Future Skies: Blockchain. In particular, CANSO highlights identity management, fleet management, airspace management and flight planning, integration of unmanned aerial systems, registration and recording of identities and tokenisation.

Blockchain provides a great opportunity to “rethink their information systems in which a shared-trusted real-time distribution of ATM-related information can pave the way for short/medium and long-term efficiency and rationalisation with the possibility of far-reaching automation and transition to the digital era of ATM”, said CANSO.

“Over time, as blockchain demonstrates proficiency and gains public trust, the entire airspace infrastructure can further leverage its benefits,” it believes. A number of recently completed projects have sought to prove the benefits of blockchain in ATM, with further work planned.

The European Union-funded SlotMachine project, the first stage of which was completed at the end of last year, explored the technology’s potential to deliver a more cost-efficient solution for slot swapping between airlines, creating a marketplace for airlines exchanging slot priorities.

The challenge with slot swapping is that prioritising flights involves confidential and sensitive information around flight cost structures, information that airlines do not wish to share with competitors. This has hampered the development of slot swapping between airlines.

The artificial intelligence-based SlotMachine project aimed to develop a more flexible, faster, scalable and semi-automated process of slot sequence transactions in a fair and trustworthy way, based on the cost structure priorities of airlines, but protecting sensitive airline data through the use of blockchain technology and secure, multi-party computation.

Essentially, the project aimed to develop a secure digital marketplace where airlines could swap flight slots without exposing commercially sensitive information. The project was led by Frequentis and involved EUROCONTROL, the Australian Institute of Technology, University of Linz and Swiss International Airlines. Eduard Gringinger, Frequentis principal scientist and consortium project lead, explained: “The consortium is formed of partners with complementary backgrounds that cover the whole value chain for slot swapping, including economic, technical and legal challenges.”

An industry-wide advisory board was established to assess the feasibility of the project, including reviewing requirements, design decisions and results. This board comprised numerous airlines, including El Al, Air Baltic, Air France, Austrian Airlines, Qantas, Ryanair and Hop; the universities of Westminster, South Australia and Passau; the European Business Aviation Association; SITA; Athens International Airport; airport group Fraport; and Swiss ANSP Skyguide.

The project used real data from Swiss to test the concept in several validation runs, said Gringinger. “We also created synthetic test data for simulation to consciously create situations to put the SlotMachine to the test. So, it was a bit of a middle ground between real and simulation – the transactions took place, but we did not make swaps. The completion of any flight swaps must be certified by the [EUROCONTROL] network manager, which of course is not possible in research projects,” he added.

Airlines will only participate in such a platform if they can be assured their data is fully protected, said Gringinger. “SlotMachine has already demonstrated that it is possible to provide a privacyfriendly environment for airspace users to exchange data. For the flight exchange credit model to work to its maximum ability, there is a need for all airlines – big and small – to find the benefits. By having more airlines participating, the more possibilities we have for optimisation,” he explained.

The project demonstrated that the privacy-preserving technology and algorithm both work and it is capable of swapping slots in the given timeframe – a couple of minutes. “What is still missing is the adoption of new processes to permit the swapping of more than two flights per day. Working with the network manager in the next stage will make it more straightforward to integrate this into the existing technology concept,” he added.

Airline response to the project has been positive, said Gringinger, with airline feedback centred around the benefits of the delay credit-based currency for swapping slots, questions around the value rates for credits and suggestions on how to speed up implementation, including an initial “lite” version for a limited number of airlines. “Airlines on the advisory board commented that the credit exchange model for flight swapping will allow airlines of all sizes to participate, even small operators that may have nothing to swap, unlike the UDPP [user driven prioritisation process] airline or hub level swapping,” he said. Gringinger expects to attract more airlines due to the potential for cost savings through predictive flight allocation and the fact it preserves airlines’ sensitive data.

Further developments with SlotMachine are planned. “The project’s success now lays the foundations for further development and real-world testing,” said Gringinger: “The goal is to deliver the results from research to the real world faster than has happened with other topics in the ATM domain.”

The consortium applied to SESAR for a follow-up project, with a decision due as this issue went to press. “We hope to be able to carry out a follow-up project to update the technology stack and involve more airlines and ANSPs. This will allow us to do real swaps between multiple airlines,” he said, adding that EUROCONTROL has already committed to a follow-up. Results will feed into the SESAR UDPP departure, which allows airlines to change the priority order of unregulated flights themselves in collaboration with airport authorities.

The project’s findings could be replicated in any part of the world, Gringinger said, with the challenge for airlines the same the world over. “The highly confidential cost structure of flights is what is preventing the open swapping of slots between airlines.”

Another European project, AICHAIN, has developed a platform for privacypreserving federated machine learning using blockchain to support operational improvements in ATM. Federated machine learning has been successfully used in other industries, including pharmaceuticals and healthcare and the AICHAIN project wanted to prove its feasibility for ATM through development of an innovative Digital Information Management (DIM) concept.

Also funded through the SESAR Joint Undertaking, AICHAIN was also completed at the end of 2022. The project involved SITA EWAS Application Services, EUROCONTROL, Swiss, SITA-owned software company GTD Air Services, technology company Nommon Solutions and Technologies and machine learning specialist Scaleout Systems.

The project recognised that gathering and sharing sensitive data from airlines could help ANSPs better predict and plan flight allocations, resulting in reduced costs and delays at airports. The partners developed a machine learning system using blockchain to facilitate this while guaranteeing data privacy and security protection.

“ATM operations could greatly benefit in terms of capacity, efficiency, predictability or safety if certain operational systems were powered by machine learning models. To train them effectively, such datadriven models need access to large highquality datasets. However, some relevant datasets owned by different ATM actors – airspace users, airports, for example – cannot be accessed through traditional data sharing mechanisms due to their privacy requirements,” the project partners reported.

Like SlotMachine, AICHAIN involved the use of commercially sensitive airline data, relating to fuel costs and staffing issues, for example. “Airlines are today reluctant to share their decision making,” said project co-ordinator, Javier Busto from SITA: “The challenge here is not just technological; it is also about encouraging stakeholder participation in building common global models for the benefit of ATM as a whole.”

The project sought to improve flight allocations by applying machine learning to sensitive and non-sensitive data in a decentralised, protected way, with the blockchain providing an audit trail and giving airlines confidence to share data. “We developed a federated machine learning system that could be trained with this sensitive data from airlines. The network manager, in this case EUROCONTROL, could then use this secured information to enhance the prediction of the flight routes chosen by the airlines and the expected takeoff times, to ensure smoother operations,” Sergio Ruiz from EUROCONTROL’s Innovation Hub explained in a CORDIS article. The project focused on two specific enduse cases relating to demand and capacity balancing – improving the prediction of estimated take-off time and flight route prediction – with improved predictability in these areas important in order to achieve better management of traffic and reducing latent capacities. This in turn, would lead to reduced ATM costs.

The project assessed the system in terms of technological feasibility, operational value and stakeholder acceptance. “Federated machine learning is a fairly mature technology, though not too well known in this sector,” said Busto. “We demonstrated that we could develop a bespoke system that fully protects privacy and generates the due trust and incentives for airlines to participate,” he added. Ensuring the machine learning-based system could be certificated for use in ATM was also investigated, with project partners working closely with aviation authorities to ensure it would satisfy aviation’s stringent requirements.

“This is a very complex domain that is still evolving,” said Ruiz. “In this way, the project has been able to exert a positive influence on the certification process, in particular when data privacy and process transparency are both required.”

The project also demonstrated the power of blockchain in terms of traceability and generating trust. Ruiz explained: “Data is not on a platform as such, but stays with the owners of the data who remain in full control. From there it can be exploited by the network manager through the federated learning platform, while privacy is protected.”

AICHAIN, which has global applicability, has attracted the interest of other airlines. In the next stage of development, the project partners would like to explore more use cases with additional stakeholders. “The AICHAIN project is evolving and we now plan larger scale experimentation with selected ATM use cases,” said SITA. Machine learning-based predictive models could deliver operational improvement in a number of areas, including curfew management; flight efficiency indicators for ATC and ATFM; and inter-modality – cargo-drone hub operations and end-to-end passenger journey operations, for example.

Ruiz said: “We’d like to see more airlines providing data in a federated and privacypreserving manner. The good news is more airlines have expressed an interest.”

Article originally published in Air Traffic Management magazine, issue 1, 2023.  Want to receive all of the latest stories as soon as they are published? Register now for your free digital subscription.