Air traffic optimisation in the cloud

Source: PASSUR

In July of this year, PASSUR and Aireon announced a partnership to enhance the options for air traffic optimization from the cloud.  This increased situational awareness is driven by advanced data analytics capabilities, machine learning (ML) and artificial intelligence (AI).

What is so different about this partnership? PASSUR is taking an existing solution which is based on information provided by the live surveillance sources and expanding it via the Aireon satellite-based flight tracking data.

Let’s take a look at what the PASSUR Ariva platform brings to the table.  Sixty-three percent of flight departures in the United States are already optimised by Ariva, showing industry acceptance to build on with new functionality. At the simplest level this is a cloud-based digital collaboration and decision support platform that airports and airlines can use to improve their gate-to-gate flight tracking.  And therefore, improve their forecasting of capacity vs. demand.  ANSPs can also benefit from this tool to help air traffic controllers pre-plan traffic, visualise traffic and develop capacity forecasting.

The collaborative situational awareness supports decision management based on weather during a specific day and suggests holding patterns allowing for the forecasting of capacity vs actual demand and ground delay parameters to be set for a specific period of time.  The cloud-based platform will allow for Aireon’s surveillance data to be supplied to any airport without the need to deploy their own infrastructure.  This data will now be expanded to include airport surface data to facilitate more accurate and actionable decision making.  As a result, prediction capabilities will be increased and more transparency between air traffic management functions will be enabled.

Historical big data within the Ariva tool provides data points across global regions including flight, airport and airspace metrics.  Predictive algorithms, ML, AI and air traffic flow management best practices help the users determine how they want to structure and present their information to aid in decision making.

More and more functionality related to the air traffic management function is moving to the cloud.  Although these are still what we would refer to as augmented intelligence – providing additional information to take decisions – or assisted intelligence – making recommendations based on the analysis of the data – these are still first steps toward a flying world where AI is playing a bigger role in managing the airspace.