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GE Aerospace’s FlightPulse app soars past 60,000 pilots as adoption accelerates

GE Aerospace has seen rapid growth in the use of its FlightPulse app, with more than 60,000 commercial pilots now using the tool — up from 40,000 a year ago — and the number expected to exceed 70,000 by the end of 2025, the company said.

The data-driven flight monitoring app, launched in partnership with Qantas in 2017, allows pilots to review their own flight performance, compare it with peers, and identify ways to improve efficiency and safety. Airlines pay GE a per-pilot licensing fee to access the platform, which has helped strengthen the company’s reputation for aviation safety and sustainability.

Qantas captain Mark Cameron said FlightPulse helps him analyze details such as takeoff and landing angles, crucial for avoiding tail strikes on smaller aircraft like the Airbus A321. The airline also uses aggregated app data to optimize flight operations, cutting fuel costs by encouraging pilots to use less reverse thrust where safe.

According to Andrew Coleman, head of GE Aerospace’s Software-as-a-Service division, FlightPulse is now used by 42 airlines, including Delta Air Lines and NetJets, with fleets ranging from a few hundred to over 15,000 pilots. Coleman said the company aims to reach 100,000 pilots by 2026, emphasizing that the app’s goal is performance improvement — not punitive monitoring.

Airlines Aim to Cut Tarmac Time with Smarter Gate Allocation

Airlines are exploring innovative ways to reduce time spent on the tarmac, with new technologies aimed at improving gate allocation. This seemingly simple task involves a complex calculation that can drastically affect aircraft taxi times, airport congestion, and even fuel emissions. According to Dr. Joseph Doetsch, quantum computing lead at Lufthansa Industry Solutions, the number of possible gate configurations is staggering, with more than 570 billion possibilities for 15 gates and 10 airplanes. Optimizing gate allocation can help ensure that travelers spend less time waiting and help airlines reduce their environmental impact.

Traditionally, gate assignments are made well in advance, often up to a year before a flight. However, final gate decisions are revisited closer to the actual travel date, with adjustments made on the day of the flight to account for delays, changing traffic conditions, and a host of other factors.

Complex Priorities and Constraints

Gate allocation requires balancing numerous priorities. As George Richardson, co-founder of AeroCloud, notes, certain airlines might prioritize gates close to their lounges or facilities, while budget carriers may opt for more cost-effective remote stands. Other considerations include flight connections, aircraft size, and the expected runway assignment. Delayed flights can add further complexity, forcing last-minute reassignment of gates and occasionally leading to flight cancellations.

Despite the complexity, many airports still rely on manual systems to manage this process. In a survey conducted by AeroCloud, 40% of airport executives admitted to using basic tools like Excel and Word documents for gate management.

Machine Learning and Smart Gating

Airlines are beginning to invest in more advanced solutions to streamline gate allocation. For instance, American Airlines introduced Smart Gating at Dallas Fort Worth International Airport. This system uses machine learning to assign arriving aircraft to the nearest available gate, minimizing taxi times. The new process, which used to take around four hours, now takes just 10 minutes and has reduced aircraft taxi times by 20%, saving about 1.4 million gallons of jet fuel annually.

Quantum Computing: The Next Frontier

Lufthansa Industry Solutions is pushing the envelope further with quantum computing. This cutting-edge technology, which uses quibits to solve complex problems much faster than traditional computers, could revolutionize gate allocation. Dr. Doetsch believes quantum computing can offer real-time, optimal solutions even as external factors change. Early trials have shown that quantum algorithms could reduce passenger transit times by nearly 50%. Although still in its early stages, quantum computing could significantly enhance airport efficiency and reduce the need for physical airport expansion.

As global airports face increasing pressure on their capacity, advanced technologies like machine learning and quantum computing may be key to maximizing existing resources and improving the overall travel experience.