Artificial Intelligence and the Aviation Industry

Introduction

For as long as we have looked up to the sky to watch birds, flight has enthralled and fascinated humans. Although several attempts had been made at building aircrafts, it wasn’t until the famous Wright Brothers launched their plane that air travel began to hold promise as a mode of transportation. Since then the aviation industry has grown as a result of advancements in technology. Before the COVID-19 pandemic halted international travel, about $607bn of revenue was recorded in 2019 with 4.5bn passengers traveling across the globe. Although that number dropped drastically to $138.5bn in 2020, steady recovery has been noted with revenue growing back to $498bn by the middle of 2022. It is projected that recovery will be complete by 2024. As such, it is necessary that continued improvements be made to ensure success. 

Along with these advances and the popularization of air travel, a host of challenges have arisen ranging from safety and maintenance of aircraft to revenue management, among others. Other concerns include the growing environmental impact of air travel with about 905 million tonnes of CO2 emitted in 2019. Fortunately, accidents have reduced over the decades with the development of advanced safety features and improved training. In addition, systems for monitoring passenger flow and predicting consumer behavior have been useful in determining flight schedules and prices. Technology has played a major role in these achievements and can continue to be employed in order to ensure success.

AI and Aviation

AI provides opportunities and methods that can help to tackle these problems and has been explored and implemented in various ways across the industry. 

Predictive Maintenance: Predictive systems on aircraft are able to provide information in real time to technicians regarding performance and health monitoring. With this, engineers are able to conduct maintenance checks on aircraft and maintain the safety of passengers and crew. Conducting fixes can also reduce the cost of repairs as problems are quickly identified and controlled.

Personnel Management: Complex activities such as scheduling and crew management which may be tedious for human departments can be carried out by AI to reduce errors. This is especially important in ensuring that working times for crew members are closely monitored in order to avoid pilot fatigue.

Autonomous Piloting: Autonomous systems similar to self-driving on cars have been launched for smooth take-off and landing. These employ the use of image recognition and sensors to respond to the surroundings and weather conditions to ensure safety. Flight management can also collect information regarding weather patterns and air traffic and use that to propose the best flight paths. 

Flight Monitoring: Vast amounts of information are collected and sent to air traffic controllers. These must be appropriately analyzed and AI can aid human workers in monitoring of flights to ensure safety. 

Fuel Efficiency: Airlines contribute large amounts to carbon emissions and pollution. ML systems can be used to achieve fuel efficiency by analyzing aircraft type, weather and travel distance. Also, systems can create new and efficient engine designs to help reduce the environmental impact of air travel. 

Revenue Management: Revenue management systems are used to analyze passenger flow and determine peak periods for travel to certain destinations. By doing this, airlines can respond to fluctuations in demand and supply and adjust their prices accordingly. 

Personalized services: Analysis of feedback collected by airlines from frequent fliers can be used to determine customer preferences and predict future behavior. This can then be applied in advertising and marketing to provide the best choices for customers. 

Conversational AI: Automated messaging regarding flight information can improve and maintain customer satisfaction. Chatbots can also be used to answer customers’ questions and provide timeline replies to enquiries. This is aimed at ensuring customer satisfaction.

Fraud Detection: Fraud detection is important in flagging financial discrepancies, false claims as well as other activities that lead to loss of revenue. Analytics can help in early detection and prevention of some of these activities.

Self-service: Although the use of self-service has been developing in recent times, social distancing during the COVID-19 pandemic drove the rise of self-service desks for check-ins and other forms of interaction. These can also collect information related to baggage and customer identification.

Conclusion

AI provides a lot of opportunities for improving various aspects of air travel. This must however be done carefully and cautiously as systems themselves are vulnerable to attacks. It must also be understood that the human touch remains necessary for certain aspects of decision making. With careful consideration given to the peculiarities of air travel, AI can be introduced to improve the industry and usher in more transformative innovation. 


References

https://www.iata.org/en/iata-repository/pressroom/fact-sheets/industry-statistics/

https://www.altexsoft.com/blog/engineering/ai-airlines/

https://addepto.com/fly-to-the-sky-with-ai-how-is-artificial-intelligence-used-in-aviation/



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