We monitor runway performance, runway occupancy time, and aircraft operations to predict safety and performance issues. We take into account the aircraft type/operator, meteorological conditions, runway configurations, threshold times, touchdown points, rotation points, line-up times, exit/entry points, excessive runway occupancy, runway over run, missed exit points, and other similar factors.
We compute aviation emissions based on a requirements-based, quantitative approach. This also includes a validation of CO2 estimates even when the data is incomplete or of unknown quality. For this, we utilise emissions modeling, aircraft performance models, mathematical modeling, statistical analysis and operational expertise.
Performance metrics and disruption management
By running continuous simulations, monitoring flights and the connections, we can assess the likelihood of potential problems and associated costs, with the aim of managing costs of disruptions in particular. Sources of data to be used will come from inter alia, network (traffic) data, status and forecast of potential air traffic congestion data (using real time traffic data), passenger inventory systems (for itineraries and connectivity data), and flight planning systems.
We examine airspace performance through relationships between sector capacity, workload and complexity. We use machine learning techniques that analyse large quantities of historical data, which lead to predictive analytics. Air traffic controller teams come away understanding the most likely scenarios to occur with information available. Our technology enables evaluation under novel flight and passenger prioritisation scenarios, plus future regulatory environments (such as changes to Regulation 261), exploring trade-offs and characterising delay through the network.
Through data mining tools, techniques and procedures, we are able to optimise fuel use in airline operations by taking into account the complexity of the scenarios which presently characterise air transport operations. This is completed through observing the airspace capacity restrictions and flow control, airport congestion, meteorological conditions, fleet and equipment mix, personnel training, and other factors.
Aviation Safety Analysis
Safety monitoring and safety intelligence can be gathered through deep analytics across different aviation data assets, including FDM, radar, network data and meteo. We develop predictive analytics for safety, including safety performance indicators for a variety of scenarios, such as: unstable approaches, real time approach congestion monitoring, airprox, lack of proper separation with terrain, or adherence to air traffic control procedures.
The Artificial Intelligence and Deep Learning platform for aviation data