Aerospace

  • Air Traffic Management (ATM)

Big Data Analytics for Air Traffic Control

Problem

  • According to International Air Transport Association (IATA):
    • Every day about 100,000 flights bring people and goods to their destination.
    • Each day more than $18.6 billion of goods travel by air, one third of all world trade by value.
    • 63 million livelihoods are supported by air transport
    • Over 3.7 billion passengers will fly this year.
  • A key aspect of this system is Air Traffic Control (ATC), vital for safely directing and navigating airplanes through the local airspace, during take-off and landing. ATC applies separation rules to the aircraft that they direct. Separation rules are used to regulate the distance between other airplanes and that aircraft by requiring a minimum distance among them. This is for increasing safety and reducing unnecessary risks for pilots and passengers.
  • To solve the saturation of the air transport system, two initiatives are in place to completely overhaul airspace and its air traffic management (ATM): Single European Sky ATM Research (SESAR) in Europe and NextGen in USA.
  • SESAR's target concept relies on a number of new key features:
    • The network operation plan, a dynamic rolling plan for continuous operations that ensures a common view of the network situation;
    • Full integration of airport operations as part of ATM and the planning process;
    • Trajectory management, reducing the constraints of airspace organization to a minimum;
    • New aircraft separation modes, allowing increased safety, capacity and efficiency;
    • System-wide information management (SWIM), securely connecting all the ATM stakeholders which will share the same data;
    • Controllers and pilots will be assisted by new automated functions to ease their workload and handle complex decision-making processes.
  • A whole set of new tools are needed to understand, model, plan, forecast and control the air operations under the new paradigms.

Solution

  • At Eris Innovation we apply traditional statistics in addition to Machine Learning techniques such as; Bayesian classification, cluster analysis or multivariate regression, employing innovative methods like Deep Neural Networks or Recursive Neural Networks to recognize patterns, classify or detect anomalies in flight data.
  • These data analytics tools are combined with powerful and realistic simulations of the aircraft dynamic behavior to obtain useful information like operation security margins, fuel consumption and flight efficiency.
  • This rich set of analysis and simulation tools allow the development of several solutions:
    • Fuel consumption from trajectory data help airlines and ATC to improve the design of efficient flight plans.
    • Pattern recognition in anomalous flights will help to detect dangerous situations in real time and will give ATC personnel more time to deal with them.
    • Efficiency indicators can be used to analyze the evolution of flight control network country by country contributing to the future integration in the European Single Sky Network.