More people than ever are interested in flying. Computers are learning how daily life affects demand flights thanks to data on everything. The mysterious airfare codes and price brands that have restricted ticket sales for decades are destroyed by AI in its most sophisticated form.
Technology suppliers can predict how much customers will pay for tickets and continuously reprize seats by weighing the data. According to an Israeli firm called Fetcherr, which runs a live-pricing engine, employing AI to calculate fares can increase an airline’s revenue by 10% or more.
The impact of COVID-19
In 2020, travel virtually ceased as governments all around the world implemented COVID-19 restrictions and locked borders. The International Air Transport Association estimates that after the pandemic, airline revenue will rebound to $782 billion in 2022, which is still below the $838 billion in 2019. Since the beginning of the financial crisis more than ten years ago, the average yearly revenue growth has been in the single digits.
For managing fares, airlines have utilised software for many years. Seat availability in various price ranges has influenced final passenger costs to some extent. After two years of lockdowns, it is harder to determine how much money passengers want to spend, thus AI aims to better match prices to that desire.
The impact of AI in aviation is still developing. The information fluxes, however, are currently too great to comprehend clearly. Every second, Fetcher alone processes numerous petabytes of data coming from all around the world in order to gauge travel demand. 500 billion pages of conventional printed text fit into one petabyte.
FMS, ATC, and ATM
The benefits of AI in aviation are becoming increasingly apparent to businesses and airports around the world. As it turns out, certain processes, including flight planning, flow management, and safety evaluation, can be automated, at least in part.
Aviation businesses can use big data to train their machine learning (ML) algorithms to take into account a variety of factors and data sources. In this approach, sophisticated ATM applications can assess the weather and air traffic to determine the best course of action based on these two important data sources.
Algorithms can become more and more efficient over time with ML. Following initial training, they become more adept at performing under real-world circumstances.
The main goal of air traffic control is to ensure everyone’s safety. As a result, ATC is normally run from the control tower, where dozens of ATC professionals direct and coordinate the landing and takeoff of neighbouring aircraft.
The work of ATC specialists is greatly eased with ML in place. Swedish LFV, which worked with IBM to develop an ATC system called Advanced auto planner, is one of the businesses developing such AI-driven ATC systems. They currently have a model that gives air traffic control directions in a Swedish en route sector, serving as the first proof-of-concept.
Additional applications
AI is mostly used to decrease costs and increase efficiency. A number of labor-intensive, previously manual tasks have been made smart and simple using AI. Predictive maintenance, personnel scheduling, and flight optimization all fall under this category.
In conclusion, AI in aviation is an interesting and rapidly expanding topic of study. We anticipate seeing a lot more incredible applications in commercial aircraft, air forces, and automobiles. As an illustration, airlines and other carriers can use AI to optimise their routes, save travel time, and enhance UX.
