Case ID: M25-042P^

Published: 2025-08-21 12:51:05

Last Updated: 1755780665


Inventor(s)

Yongming Liu
Yutian Pang
Jueming Hu

Technology categories

Applied TechnologiesArtificial Intelligence/Machine LearningPhysical Science

Licensing Contacts

Physical Sciences Team

Machine Learning-Enhanced Aircraft Landing Schedule Algorithm

Background

The civil aviation industry is losing air traffic control talents during a time when the need for maintaining daily operations is surging. This situation leads to increased operational costs, higher safety concerns, an elevated workload for air traffic controllers, and frequent flight delays. It is reported that 20% of the civil flights in the U.S. were delayed from 2010 to 2018, and the annual cost of delays before the pandemic is estimated to be $30 billion. The initial flight delays come from various resources (e.g., extreme weather conditions, carrier and controller issues) and can propagate through several hours. This urges the automation and digitization of the aviation industry which heavily relies on innovative data-driven modeling techniques.

Invention Description

Researchers at Arizona State University have developed a technology that utilizes advanced algorithms and machine learning to optimize data processing tasks for aircraft landing schedules. This technology is designed to integrate seamlessly with existing IT infrastructures, providing an immediate boost to computational speeds and data handling capabilities. By using historical flight data to train the model, it predicts arrival times and separation requirements which are used as inputs to the optimization algorithm to determine an optimal landing sequence. This data-driven approach allows for better handling of uncertainties and can adapt to different scenarios, leading to more reliable and efficient schedules. The system also incorporates safety constraints directly into its optimization process, ensuring that the proposed schedules maintain required separation between aircraft.

Potential Applications:

  • Airport Capacity and Infrastructure Planning
  • Aviation & Air Traffic Management
  • Decision Support Systems (DSS)

Benefits and Advantages:

  • Versatile – Easy integration with existing systems
  • Expense reducing – Reduces operational costs by enhancing efficiency
  • Scalable – Applicable across various industries and systems