Going Digital

In spite of a 7.9 per cent increase in traffic, 2017 was the safest year in aviation history and during the next 15 years, we can expect even higher demand with a twofold increase in air traffic.
Maintaining or improving the safe aviation record and ensuring the fair, safe and secure integration of UAS business will require more data and services, writes Nicolas Suarez.
Addressing this challenge, the workshop “Data Driven ATM: Going Digital” at this year’s World ATM Congress described the state of the art and debated the areas of interest and key technologies needed to be developed in the future, regarding the use of Information Networking and Big Data techniques in the Air Traffic Management area.
Discussions took place within a panel that presented and debated the trends and issues relevant to the different organisations. The general feeling is that information networking—and along with it, data science—is leading us into the third transformation of the airspace and ATM.
Along with this path, the panel discussed a large number of ideas (such as the development of predictive systems based on data science), focusing more on five: the availability of data, increasing efficiency, supporting application through appropriate business models, developing knowledge and insight, and implementing a seamless passenger experience.
Data Science is based on the availability of data
Perhaps this is the reason that Data Sharing was one of the most discussed topics. Sharing data not only requires the development of a secure environment, it also requires the existence of a common information model that facilitates access to the information. Furthermore, sharing data implies the existence of a business model that ensures that everybody in the supply chain (from the data provider to the information user) gets something in return.
During the discussions, ideas such as the creation of a third party, independent business entity in charge of the data or the creation of a “data space” were raised. All ideas acknowledged the need to have a secure environment, in which access was granted only to the people authorised to have it.
Using data science to increase the efficiency of the operations of providers and clients

 Ongoing work in the different organisations is already leading to increases in efficiency in aspects such as crew management, in-flight path optimisation, prediction of the required landing requirements or decision support tools for non-time critical systems. Efficiency will not be limited “just” to do things better but to create and exploit better the information of the ATM system

Supporting the use of data science applications through appropriate business models
Data is not free. There are costs associated with its collection, its cleansing, its transformation into information and its exploitation. We need to know who pays for the processing of data into information and we need to be able to identify clearly how value can be accrued and costs recovered. Business models will ensure that the value of the tools and applications will be shared amongst those that contribute to its generation.
Developing knowledge and insight of the ATM system through data science
Data Science will lead to smarter processes that will support increased collaboration and innovation. Furthermore, smarter processes will support higher infrastructure elasticity leading to a better use of available resources.
Implementing a seamless passenger experience

Data science can support the transition of the passenger between the different stages of her/his trip. Data science can also provide insightful information on the airport, the departure/arrival times, travel arrangements, etc. This information will be based on data acquired automatically (once authorised by the passenger) from different sources such as electronic boarding passes, mobile phones, etc.

The need for visual analytics/visual interfaces

 If algorithms are ever to take the centre stage from humans and take progressively more autonomous decisions to cope with the expected increases in demand in traffic, full data understanding and trust in models becomes absolutely crucial. The human expert must still be part of the loop imparting her/his tacit knowledge on all stages of data-driven processes where it is beneficial. Both these aspects require suitable analysis tools and interfaces.

In summary

As the panel discussions showed, there is a strong call for cooperation between R&D organisations and ATM industries in projects with mutual added value; R&D organisations need access to ATM data and ATM industries (both ground and airborne) need improvements in decision support tools, visualisations of complex data and model output, etc.
As mentioned during the workshop: “data is for engineers and information for operational people”. As an industry, we need to ensure that we foster and implement a successful transition from data to information. Information Networking and Data science have come to stay. We need to move forward and stimulate research and development in these areas to ensure that our companies and organisation create the value that society expects from us.