Using Machine Learning in Airline Operations Control (AOC) Applying machine learning (ML) in an Airline Operations Centre (AOC) is a highly promising and complex domain. The AOC functions as the command hub for airline operations, where operators handle an overwhelming amount of real-time data to ensure smooth execution of flights. Supporting these professionals with intelligent systems means freeing them up to focus on strategic decisions rather than routine monitoring. The Problem: Flight Diversions and Information Delays Let’s consider a common scenario: when a flight undergoes a diversion or a go-around, the first point of communication typically comes from Air Traffic Control (ATC). However, not all airlines have established direct communication channels with every airport across their network. This delay in acquiring diversion information often results in inefficient logistical decisions. Imagine an aircraft scheduled to deliver cargo to a city. The cargo is meant to be picked up...
If there is an accident nearby or a gun shot, you notice that immediately. The tell tell signs would be a croud for an accident and sound for a gun shot. This can also be applied for a person in a bike. No problem there, he could notice something is happening and could react to it. However, if we scale this situtation to such extent that you cannot see things any more. At an altitute of 33k feets, you will never know what is happening on the ground. Maybe there is a Zombie apoclips, a Volcanic erruption, a bombing. To inform the Pilots (Airmen) about the real time Hazards that are not always forecasted or foreseen a system of communication was developed called NOTAM- Notice to Airmen. And the natural hazards generally fall under SNOWTAM. A NOTAM warns pilot of potential hazards along a flight route or at a location that could affect the safety of the flight. NOTAM is called the Alphabet Vomit, because at first glance it looks chaotic. However complex it may be, they are real...