Revolution in the Skies: How Machine Learning is Reshaping Future Air Travel
Authored by: Rushil Johal , Deputy Director of Technology, Fidutam
Edited by: Leher Gulati , Editorial Director, Fidutam
Dawn of the AI Boom in Aviation
In an era where technology relentlessly pushes boundaries, machine learning emerges as a pivotal force in commercial aviation. This adaptable technology redefines industry norms, offering groundbreaking solutions to challenges historically plaguing flight. Machine Learning can resolve ubiquitous problems in safety, operational efficiency, customer satisfaction, and atmospheric environment in the commercial airline industry.
Safety in the Skies: The Role of Machine Learning in Predictive Maintenance
Machine Learning (ML) is vital in bolstering the importance of air safety through predictive maintenance. Traditional maintenance models, predominantly reactive, often lag in preempting potential issues. However, ML algorithms meticulously analyze data from aircraft sensors and maintenance records, foreseeing potential failures and mitigating accidents, delays, and high costs associated with unscheduled maintenance. For example, Airbus's Skywise platform reduces aircraft-on-ground times by up to 30% by monitoring individual aircraft's long-term health and reliability, such as flight cycles and inoperative minor equipment, which are difficult to detect manually.?
Beyond predictive maintenance, ML also enhances safety through real-time monitoring and analysis of flight data in the air, enabling pilots and ground staff to make informed decisions during 'safety-critical times,' such as rare engine compressor stalls, crew incapacitation, or fuel shortage. Additionally, General Electric Aviation, the world’s second-largest engine supplier, uses ML to analyze data from over 35,000 engines in service, predicting failures before they occur and significantly reducing downtime and operational costs. This proactive approach extends the lifespan of aircraft components, ensuring they are serviced or replaced at optimal intervals, further reducing long-term operational costs (GE Aviation). Moreover, ML aids in simulating and analyzing flight conditions and pilot behavior in real-time, enhancing decision-making skills in critical situations.
Machine Learning Driving Ground Operational Efficiency
Flight operations also benefit from ML, particularly in terms of efficiency. Operational efficiency in aviation transcends punctual departures and arrivals, encompassing significant aspects like fuel consumption and environmental impact. ML algorithms adeptly analyze large sets of real-time data to recommend approximately the most efficient flight paths. These recommendations consider many variables, including weather patterns, air traffic, and specific aircraft performance characteristics. The result is a marked improvement in routing efficiency, leading to substantial fuel savings and a consequent reduction in carbon emissions. In fact, Boeing estimates ML-enabled flight optimization slashes fuel usage by 10%, a considerable achievement aligning with the industry's growing environmental consciousness (Altus 5).?
Concerning punctuality, Dallas Fort Worth International (DFW), the third-busiest airport in the world by aircraft movement, utilizes ML in optimizing gate assignments and aircraft turnaround times to reduce flight delays and late penalties. This optimization not only improves the operational flow but also significantly reduces carbon emissions associated with idling aircraft, where engines input the most inefficient fuel per passenger per mile economy. Another severe problem of ground handling, especially in small or crowded airports worldwide, is limited tarmac area. Thus, American start-up Fyve By combats this by maximizing multiple neural networks of real-time digital image recognition to detect aircraft when moving in maintenance hangers, preventing accidental ground and expensive fuselage damage (Singh).
Personalization at Altitude: Elevating Passengers’ Journeys with ML
ML has also transformed personalizing passenger experiences. Maximizing customer satisfaction is a top priority for airlines in the fiercely competitive aviation market. ML empowers airlines to tailor their services to individual passengers' preferences across versatile touchpoints and vast target audiences. This includes offering luxurious booking options for upper socioeconomic classes, providing point-to-point transit, or simplified pricing models where low-cost carriers excel.nA 2020 Omnia AI Report by Deloitte highlights how airlines employing AI-driven personalization strategies have recorded a 15% uptick in passenger satisfaction scores (Jaffery 11).?
A hidden application is how airlines 'choose' new destinations by calculating, detecting buying patterns, and analyzing historical passenger demand between unconnected cities with no direct flights for monopolistic revenue. Referred to as "dynamic pricing," popular travel websites, such as Google Flights, Expedia, and Booking.com, often legally but discreetly sell millions of users’ data to airlines to implement in their ML business strategy (Shartsis). Such nuanced curations elevate the travel experience and encourage repeat patronage, cementing customer loyalty.?
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Expanding on this, the 2023 Skytrax Airline of the Year, Singapore Airlines' KrisLab, uses ML to offer personalized in-flight services, enhancing passenger satisfaction by tailoring entertainment and meal choices. Additionally, Dutch flag carrier KLM was one of the first airlines to take advantage of AI-powered chatbots to handle bookings, provide flight updates, and resolve customer queries efficiently, offering a seamless customer service experience.
Eco-Friendly Skies: ML's Fight Against Climate Change
Moreover, ML is at the forefront of combating environmental issues as traditional methods have struggled to mitigate the commercial and private airline industry's carbon footprint. However, sophisticated data analysis and predictive modeling offer a new pathway by analyzing vast amounts of data on weather patterns, air traffic, fuel consumption reduction, and aircraft performance to identify the most fuel-efficient flight routes. For instance, US-based Alaska Airlines has implemented an ML-based flight planning system, Flyways, that analyzes countless data points, including weather patterns, aircraft weight, and air traffic, to determine the most fuel-efficient flight paths (Schlosser).?
Besides flight planning, Europe's second-largest low-cost carrier, EasyJet, has partnered with American start-up Wright Electric to leverage ML in designing electric-powered aircraft for short-haul flights carrying approximately 200 passengers by 2035 (Kolirin). By analyzing vast datasets on flight operations and energy requirements, this collaboration aims to bring sustainable, zero-emission aircraft to the market, demonstrating a concrete step towards reducing the environmental impact of air travel.
A New Era of Air Travel Powered by ML
Overall, integrating machine learning in commercial aviation addresses core safety, operational efficiency, passenger satisfaction challenges, and environmental concerns. The evolution of AI as federal government agencies or trade associations, such as the Federal Aviation Administration (FAA) or International Air Transport Association (IATA), are partnering with local data science companies amid the AI boom is a fundamental pillar in the unimaginable future potential of public aviation. Extending far beyond current applications, air travel is not just a means of transport but an experience in itself.
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About the Author
Rushil Johal is the Deputy Director of Technology at Fidutam and a first-year student studying Computational and Data Science at George Mason University in Fairfax, VA. In 2022, he founded Aviate International, a youth-led 501(c)3 providing first-hand STEM opportunities for less-resourced students interested in pursuing careers in the data science and airline industry. Outside aviation, he is passionate and involved in other AI projects, especially applying in healthcare and transportation, such as Director of Programs at GMU's Inquisitive Inventors, Director of the Americas at the Global Initiative for Digital Rights, and formerly an Assistant Radiology (MRI) Researcher at the Penn Medicine.
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