AI and Data Science in Aviation Industry: 5 Real-life Use Cases

Are We Ready for Pilotless Planes?

Introduction to the Discussion

• The question of flying without a human pilot sparked lively discussions on social media, particularly Facebook, with emotional reactions from users.

• Some individuals expressed a greater comfort level with driverless cars compared to pilotless planes, possibly due to perceived safety differences between road and air travel.

Current State of Aviation Technology

• While prototypes of self-piloted passenger air vehicles by Boeing and Airbus have completed test flights, full commercial implementation is still years away due to certification and testing requirements.

• Airlines are currently utilizing AI and data science for operational efficiencies rather than fully autonomous flight systems.

Use Cases of AI in Aviation

Revenue Management and Route Planning

• Airlines analyze traveler demand for specific city pairs using sophisticated data science methods, which can include behavioral data from online searches and social media interactions.

• Machine learning techniques have been employed by companies like Skyscanner to group origins and destinations based on various parameters, revealing surprising insights about travel preferences.

Demand Prediction Based on Events

• Events such as festivals or conferences can create short-term spikes in travel demand; airlines adjust fares accordingly using event data analytics.

• Qantas utilizes ranking algorithms that correlate historical booking patterns with event schedules to predict how events impact traveler demand.

Food Supply Management

• Airlines face challenges in estimating onboard food supplies accurately; miscalculations lead to significant waste—easyJet's analysis revealed substantial losses due to over-preparation of meals.

• A new algorithm developed by easyJet's data scientists improved demand predictions significantly, reducing waste while saving costs for the airline.

Environmental Considerations in Fuel Consumption

Reducing Carbon Emissions

• Commercial aviation accounted for 2.4% of global CO2 emissions in 2018; however, emissions increased by 32% over five years, prompting airlines to seek fuel efficiency improvements.

• Southwest Airlines developed predictive models that drastically improved fuel consumption forecasting accuracy while reducing the time analysts spent generating these forecasts from days to minutes.

Facial Recognition Technology

• Airlines are increasingly adopting facial recognition technology at boarding gates as part of their biometric systems aimed at enhancing passenger experience through streamlined processes.

Biometric Technology in Air Travel

Overview of Biometric Scanning Process

• The equipment scans travelers' faces and matches them with photos stored in border control agency databases, including those from passports and visas.

• Government agencies like US Customs and Border Protection emphasize that this technology enhances the travel experience by making it faster and safer.

• Delta Airlines opened a biometric terminal at Atlanta Airport in November 2018, claiming it to be the first of its kind in the U.S.

• By mid-December 2018, Delta began testing biometric boarding across all 14 international gates at Detroit's McNamara terminal.

• A survey revealed that 70% of travelers found the biometric boarding appealing, with 72% preferring it over traditional methods.

Impact of Delays on Airline Operations

• Turnaround time refers to the interval between an airplane landing and its next departure; delays can lead to significant financial losses for airlines.

• In 2018, U.S. passenger airlines lost an average of $74.20 per minute due to delays caused by servicing issues, which accounted for 5.8% of all delayed flights.

Technological Solutions for Efficiency

• Startups like Suruc provide software that processes video streams from airfields using image recognition algorithms to monitor aircraft preparation in real-time.

• Lufthansa's deep turnaround solution also utilizes AI to analyze video data and keep users updated on airplane servicing activities as they happen.

Data Science Applications in Aviation

• Airlines are leveraging data science and machine learning to assess passenger demand across routes, optimizing ground handling and fueling operations.

• New distribution technologies may enable airlines to personalize offers for individual travelers based on their preferences and behaviors.