Identification of airspaces with increased coordination effort based on radar data
Authors: Sören Holzenkamp, Martin Jung
Abstract: Artificial intelligence (AI) systems can be beneficial in various disciplines such as medicine, space travel or air transport. The Project “Collaboration of aviation operators and AI systems” (LOKI) of the German Aerospace Center (DLR) aims to develop guidelines for a human-centered design of communication and also collaboration between users and AI systems. The Project focusses on areas of activity in air traffic management where operators work together collaboratively. To identify the potential for AI support of air traffic controllers as well as pilots, information about the coordination effort of aircrafts for air traffic controllers in the European airspace is needed. The aim of this paper is to identify areas of increased coordination effort for air traffic controllers based on four-dimensional radar data. Here, AI could be advantageous for air traffic management.For this purpose, we used flight tracking data from a network of ADS-B receivers. The data includes all flights in the upper European airspace in September 2019 and has a resolution of one data point per minute. First, the data was pre-processed and visualized. Afterwards three criteria for detecting possible communications between pilots and controllers were applied to the data. The first criterion examines the frequency of climbs and descents in the course of a flight. The second one analyses the changes in flight direction in the flight trajectories. The third criterion identifies aircraft that fall below a minimum vertical and lateral separation between each other. The Python programming language and various data science libraries were used to apply the criteria to the data. The result is a spatio-temporal cadastre with entries of possible controller communication which shows that relatively large areas with a high coordination effort for air traffic management controllers exist in Europe. These areas are mostly located in Central Western Europe and UK, but also in Spain, Portugal and Russia, inter alia. In reality, the coordination effort is probably even higher than in this model. Against this background, it is reasonable to conclude that the potential for using AI in air traffic management is rather high and that the use of AI can be beneficial for ATM operations in Europe.
Keywords: Air Traffic Management, Data Mining, Radar Data, AI, Air Traffic Controller, Data Science, Python
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