AI Traffic

OUR GOALS

Machine Learning algoritms for predicting air traffic network performance during adverse weather

In recent years, convective weather has been the cause of significant delays in the European airspace. With climate experts anticipating the frequency and intensity of convective weather to increase in the future, it is necessary to find solutions that mitigate the impact of convective weather events on the airspace system. Analysis of historical air traffic and weather data will provide valuable insight on how to deal with disruptive convective events in the future. We propose a methodology for processing and integrating historic traffic and weather data to enable the use of machine learning algorithms to predict network performance during adverse weather. Regression and classification supervised learning algorithms can be trained to predict airspace performance characteristics such as entry count, number of flights impacted by weather regulations, and if a weather regulation is active.

Convective weather is a major source of disruption to air traffic operations responsible for roughly one third of en-route delay in the network. Understanding how weather impact air traffic flows is an important first step in improving Air Traffic Flow Management operations during convective weather. By means of a convolutional neural network, we are able to exploit the spatial correlation between convective weather, traffic demand and ATFM weather regulations patterns to automatically predict the location of hotspots (high congested areas of regulated flights) in the European airspace network.