Impact of Off-Block Time Uncertainty on the Control of Airport Surface Operations

• Main Authors: Badrinath, Sandeep (Author), Balakrishnan, Hamsa (Author), Joback, Emily F. (Author), Reynolds, Tom (Author)
• Other Authors: Lincoln Laboratory (Contributor), Massachusetts Institute of Technology. Department of Aeronautics and Astronautics (Contributor)
• Format: Article
• Language: English
• Published: Institute for Operations Research and the Management Sciences (INFORMS), 2021-04-09T18:34:59Z.
• Subjects: Article
• Online Access: Get fulltext

 Congestion at major airports worldwide results in increased taxi times, fuel burn, and emissions. Regulating the pushback of aircraft from their gates, also known as departure metering, is a promising approach to mitigating surface congestion. Departure metering algorithms require models of airport surface traffic and knowledge of when a flight would be to be ready for pushback, which is called the earliest off-block time (EOBT). While EOBTs are known to be inaccurate due to several reasons, there has been little prior research on characterizing EOBT uncertainty and its impact on departure metering. We present a new class of queuing network models for the airport surface that are capable of capturing congestion at multiple locations. We demonstrate our modeling approach using operational data from three major U.S. airports: Newark Liberty International Airport, Dallas/Fort Worth International Airport, and Charlotte Douglas International Airport. We analyze the current levels of uncertainty in the EOBT information published by the airlines and conduct a parametric analysis of the reduction in departure metering benefits due to errors in the EOBT information. Our analysis indicates that the current levels of EOBT uncertainty lead to a 50% reduction in benefits at some airports when compared with an ideal case with no EOBT uncertainty. Two approaches to departure metering are considered: the National Aeronautics and Space Administration's Airspace Technology Demonstration-2 logic and a new optimal control approach. We show that our queuing network models can help design and evaluate both approaches and that the optimal control approach is more effective in accommodating EOBT uncertainty while maintaining runway utilization.