AEM Research Associate improves airport capacity through air traffic control
- Airport capacity constraint represents one of the biggest challenges to accommodate the increased air traffic as envisioned in the Next Generation Air Transportation System. The National Transportation Safety Board has acknowledged the ability to detect whether an aircraft is in the correct place on a taxiway at the correct time is essential for maintaining the safety and efficiency of airport operations. In current air traffic control (ATC) system, this task is accomplished by the air traffic controllers - a group of people sitting in the ground station and issuing clearances to the pilots via voice communication.
- Individual aircraft need to be authorized by the ground ATC controllers before they can move from the gates and they will need another authorization near the runway thresholds before taking off. In sequencing and scheduling aircraft motion, tower ATC controllers develop some estimates of the times that individual aircraft would require to move from gates to runway thresholds. This human-centered system architecture is too limited to handle the rapid growth of air traffic and pushes the controllers’ working load to the extreme. As a result, it is highly desirable to develop computer automation tools to assist the controllers’ decision making and enable the maximum throughput and capacity in the airport environment.
AEM Research Associate, Dr. Qian Zheng, also the Project Leader in the Air Traffic Control group, has pioneered the technical design and evaluation of taxi conformance monitoring system for surface operation.
With collaboration with Mosiac ATM INc., Zheng and AEM faculty member Professor Yiyuan J. Zhao, organizer of the ATC group, successfully accomplished the project “Surface 4-D trajectory modeling and taxi conformance monitoring” funded by NASA. The developed system automatically monitors the aircraft movement and determines whether it will be able to timely pass the specified checkpoints or it will be either ahead of or behind of the schedule in the near future. In the latter case, the system would estimate the likelihood and the likely time of the excursion. Their design has been applied to analyze data from simulated taxi studies at Dallas-Fort Worth airport, and implemented within NASA’s surface simulation platform and exercised in a shadow mode against live data. The results have shown promising and significant improvements comparing with current airport surface management system (SMS). Their achievement is original and highly regarded, providing a solid foundation that enables innovative research in surface traffic planning and scheduling for the foreseeable future.