How Artificial Intelligence Is Used in Air Traffic Control (ATC)

Author(s): AlishaS

Originally published on Towards AI.

Overview

In recent years, air traffic has become a serious issue in the world. Delays in air traffic are caused by factors such as air system delays, security delays, airline delays, late aircraft delays, and weather delays.

Air Traffic Control (ATC) will become more complex in the future decades as aviation grows and becomes more complex, and it must be improved to ensure aviation safety. Nowadays, Artificial Intelligence (AI) plays an important role in data management and ATC decision-making.

AI has been recognized as having great potential in controlling ATC systems by providing advanced air traffic management systems, optimized paths, decision support tools, and UAVs.

These technologies will not only reduce risks but will also help companies by providing customer satisfaction. In this article, we’ll look over some challenges faced by Air Traffic Control and how AI is helping the aviation industry in countering these challenges.

Air Traffic Control (ATC)

Air Traffic Control (ATC) is a system that is responsible for the safe and efficient movement of aircraft in the airspace. ATC is responsible for ensuring that aircraft are separated from one another and that they are guided to their destinations safely and efficiently.

ATC is typically provided by government agencies, such as the Federal Aviation Administration (FAA) in the United States or Eurocontrol in Europe. ATC systems use a combination of radar, communications, and other technologies to track and manage aircraft in the airspace.

ATC controllers are responsible for coordinating the movement of aircraft in the airspace, issuing instructions to pilots, and monitoring weather conditions and other factors that may impact aircraft operations. They also work closely with other air traffic control centers and other organizations to ensure the safe and efficient movement of aircraft.

What are the challenges faced by Air Traffic Control (ATC)?

1. Increase demand for air control: Air traffic has expanded substantially in recent years due to the growth of the airline industry and the development of new airports and runways. It has put additional pressure on the air traffic control system, which manages thousands of flights per day while also maintaining safety and reducing delays.
2. Outdated technology: ATC needs to modernize and upgrade existing systems as many ATC systems are based on outdated technology, which can limit their ability to effectively manage traffic and increase the risk of errors and accidents. Additionally, with the increasing use of new technologies such as drones, airspace is becoming more complex, and ATC systems must adapt to keep pace.
3. The human factor: ATC relies heavily on human controllers to make decisions and manage traffic. Human errors can occur, and it is important to have systems in place to detect and mitigate these errors. Furthermore, ATC controllers are facing high workloads and stress, which can lead to fatigue, making them more prone to errors.
4. Weather: Weather is a significant challenge for Air Traffic Control (ATC) systems, as it can create unpredictable conditions that can impact traffic flow and increase the risk of accidents. Weather conditions such as storms, thunderstorms, icing, and turbulence can disrupt normal flight operations and affect the performance of ATC systems, leading to delays and increased holding times for aircraft, reduced capacity in the airspace, and increased workloads for controllers.
5. Frequency congestion: A single radio frequency can only handle a certain number of radio messages in a specific amount of time. The length of each message and its answer will determine the maximum number of messages. A pilot should ideally be able to send a message at any moment and receive a rapid response. The frequency becomes congested as radio traffic exceeds the ideal limit. The pilot must wait for a communication break to send a message and may have to wait for a response from the ATC, who has to judge different priorities.
6. Communication gap: Pilots and controllers may speak different languages and may use different technical terms or acronyms, which can create confusion and misunderstandings. Also, non-native speakers may not fully understand the instructions, especially in case of emergency or abnormal situations. Moreover, communication can also be affected by weather conditions or other external factors, such as interference or signal blockages, which can make it difficult for controllers and pilots to hear or understand each other.

Uses of Artificial Intelligence in Air Traffic Control

Artificial intelligence (AI) is used in several ways to improve the efficiency and safety of air traffic control (ATC) operations. Some of the ways that AI is being used in ATC include:

Traffic Management Systems

Artificial intelligence (AI) is being increasingly used in traffic management systems to improve the efficiency and safety of traffic flow. One of the main ways in which AI is being used in traffic management systems is through the use of advanced traffic prediction and optimization algorithms.

These algorithms generally make use of data science and python to analyze real-time data on traffic flow, weather conditions, and other factors to predict traffic congestion and suggest alternative routes or traffic control measures reduce flight delays.

Nowadays, AI is also being used in traffic management through the use of Intelligent Transportation Systems (ITS), which are a set of technologies that are used to improve the safety, efficiency, and sustainability of transportation systems.

ITS technologies can include the use of cameras, sensors, and other devices to monitor and control traffic flow, as well as the use of advanced algorithms to analyze data and provide real-time information to pilots and traffic managers.

Demand Prediction

Demand prediction is used to forecast the number of aircraft that will be flying in specific airspace at a specific time. This information is crucial for ATC, as it allows controllers to plan and manage traffic flow, and to ensure that there is enough capacity in the airspace to handle the expected demand.

Time series analysis is one method by which AI can be employed for demand prediction in ATC. This technique involves analyzing historical data on flight schedules, passenger demand, and other factors to identify patterns and trends, which can then be used to predict future demand.

By using machine learning algorithms such as neural networks, we can analyze large amounts of data and trends that are not easily visible to humans.

AI can also be integrated with other technologies, such as Automatic Dependent Surveillance-Broadcast (ADS-B) and data link communications (DLC), to provide real-time data on aircraft positions, allowing for more accurate demand predictions.

Optimal Sectorization

Sectorization is the practice of splitting airspace into smaller, more controllable portions known as sectors. Each sector is usually controlled by a single air traffic controller who is in charge of managing the movement of aircraft within that sector.

Optimal sectorization is the method of determining the most efficient and effective way to split airspace into sectors in order to increase airspace safety and efficiency.

AI modeling techniques and ML mathematical optimization algorithms can be used to provide optimal dynamic sector configuration by analyzing data on traffic volume, weather conditions, terrain, and other factors and then determining the most efficient sector boundaries.

Optimization of the flight path

Global aviation accounts for about 2% of anthropogenic carbon dioxide (CO2) and greenhouse gas emissions. That’s why aircraft manufacturers and carriers try to increase fuel efficiency.

Well, it’s not only environmental concerns but so are financial issues that are driving airline sector companies to adopt technology to decrease carbon emissions. According to Investopedia, fuel accounts for 10 to 12 percent of an airline’s operating expenditures.

Airlines use AI systems with built-in machine learning algorithms to collect and analyze flight data such as route lengths and altitudes, aircraft type and weight, weather, and so on. Based on collected data, AI systems evaluate the right quantity of fuel needed for a flight to complete the entire journey.

Airplane Maintenance

Airplane maintenance is a difficult task that may cost a fortune if done incorrectly. Today, many airlines are switching from corrective to predictive maintenance.

Airlines can use artificial intelligence to predict probable maintenance faults before they occur. AI can detect potential issues using data from in-service aircraft. AI assists in triggering real-time repairs, reducing the need for routine maintenance.

An airline can reduce costs associated with accelerated transportation of parts, overtime compensation for employees, and unplanned maintenance by implementing predictive maintenance. If a technical issue occurs, maintenance staff would be able to respond faster with workflow management software.

Airlines using AI platforms to enhance operations

As you can see, there are many ways through which airlines can use AI to automate their operations, lower expenses, provide safety, and improve customer experience. Below, we’ll discuss some real-life examples.

Thales AI-based platform

Thales is a global technology company that provides a wide range of products and services for the aerospace, defense, transportation, and security industries. Thales is involved in the development of artificial intelligence (AI)-based systems for air traffic control (ATC).

It is present in 85 places globally and provides controllers with a more precise prediction of flight trajectories, air traffic flow, and predicted flight take-off and arrival times.

One of the AI-based platforms developed by Thales is the “NeoLink” platform, which can analyze real-time data on aircraft positions, weather conditions, and other factors to predict traffic flow and congestion and provide controllers with real-time information and recommendations for managing traffic.

Thales also offers the “NeoNav” platform, which is an advanced navigation system for unmanned aerial vehicles (UAVs) that uses AI and machine learning techniques to provide autonomous navigation and decision-making capabilities.

Thales also helps airlines in providing passengers with a smooth experience. AI and Big Data are used to keep track of passengers’ interests and offer them personalized recommendations such as continuous in-flight entertainment, targeted advertising, and shopping.

Flyways AI platform

Alaska Airlines recently announced a collaboration with Airspace Intelligence and signed a multi-year contract for its Flyways AI platform. The current airline computer systems are incapable of consolidating all information and changing situations into a single unified map.

On the other hand, Flyways can forecast future events and handle exceptions by processing massive amounts of data more rapidly and precisely.

Flyways AI is a machine learning and artificial intelligence-powered 4D mapping, forecasting, and recommendation technology used for commercial aviation operations. It provides actionable advice to pilots when it finds a better route avoiding turbulence or a more efficient path.

The pilot then decides whether or not to accept and apply the suggested solution. In accordance with existing FAA standards, pilots always make the final decision.

Flyways AI helps pilots in performing smartly by recommending the most efficient and safest routes. It also helps to streamline traffic movement, which reduces greenhouse gas emissions (GHG) and fuel consumption.

Skywise AI platform

Skywise is an AI-powered open data platform developed by Airbus for aviation that uses big data analytics and artificial intelligence (AI) to optimize the performance and efficiency of airlines and airports.

Skywise contributes to airlines and suppliers by providing a powerful platform that connects multiple systems that were previously disconnected in most airlines. Airlines can create insights with Skywise analytical tools in both code-free and code-based situations.

Airbus developed this platform to conduct observation tasks such as computer vision, natural language processing, time series analysis, prediction, and decision-making. According to the head advisor of Airbus, Skywise helped Airbus during the COVID-19 outbreak by assisting them in analyzing air traffic fluctuations and flight restrictions.

Conclusion

Air Traffic Control (ATC) is a complex and critical system that faces many existing challenges, such as increasing demand for air travel, the need to modernize and upgrade existing systems, the human factor, weather, and frequency congestion.

These challenges must be addressed to ensure the safety of passengers because airplane accidents occur in airspace and often result in death. Air traffic can be controlled or avoided by predicting airline system parameters.

Artificial Intelligence has great potential to control the ATC system by providing advanced traffic management systems, automation, decision support tools, and UAVs.

These technologies improve safety, efficiency, and capacity while decreasing demands on human operators and delivering real-time data to help them make better decisions. As we move forward, AI will continue to play an important role in the future of Air Traffic Control.

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How Artificial Intelligence Is Used in Air Traffic Control (ATC) was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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