[Paper Review] Collaborative Virtual Queue: Fair Management of Congested Departure Operations and Benefit Analysis

Pierrick Burgain, Éric Féron+1 more|ArXiv.org|Jul 3, 2008

Air Traffic Management and Optimization7 references16 citations

TL;DR

This paper proposes the Collaborative Virtual Queue (CVQ), a decentralized framework enabling airlines to collaboratively manage departure sequencing during congestion by leveraging virtual holding at gates. By allowing real-time intra-airline reordering—such as prioritizing heavy aircraft—CVQ reduces average passenger waiting time by up to 15% and improves wheels-off time predictability without government intervention, enhancing operational flexibility within the NextGen air traffic system.

ABSTRACT

Due to the stochastic nature of departure operations, working at full capacity makes major US airports very sensitive to uncertainties. Consequently, airport ground operations face critically congested taxiways and long runway queues. In this report, we show how improved management of departure operations from the ready-to-push-back time to the wheels-off time can potentially yield significant benefits to airlines and air traffic services. We develop a Collaborative Virtual Queue to enable better optimization capabilities during congested situations while taking into account the laissez-faire competitive environment. Results are evaluated using a departure system model, validated using current statistics and previous studies. First, the Collaborative Virtual Queue enables keeping aircraft away from runway queues, which increases wheels-off time predictability. Second, holding aircraft enables last-minute intra-airline flight switching. This creates new optimization capabilities for airlines i.e. it gives airlines the flexibility to prioritize their flight sequence in real-time. These capabilities are illustrated by the trade-off between minimizing the average passenger waiting time and minimizing the level of unfairness between aircraft of the same airline. For instance, airlines could choose to decrease by up to 15% their average passenger waiting time by prioritizing heavy planes over small planes when the taxiway system is congested.

Motivation & Objective

• To address the challenge of increasing airport congestion and taxi-out time uncertainty at major U.S. airports under full-capacity operations.
• To develop a collaborative, non-regulatory mechanism that enhances scheduling flexibility during departure bottlenecks.
• To evaluate how intra-airline flight reordering via virtual queuing can reduce average passenger waiting time while managing fairness across aircraft types.
• To assess the impact of airline distribution (monopoly vs. multi-airline) on optimization potential and system performance.
• To align the CVQ concept with NextGen goals by reducing reliance on government control and improving operational predictability.

Proposed method

• The CVQ model simulates departure operations from push-back to wheels-off, using a time-continuous, stochastic queueing framework to represent taxiway congestion.
• It introduces a virtual queueing mechanism that allows airlines to delay actual taxi-out while maintaining a prioritized sequence for real-time reordering.
• The system uses a trade-off function between minimizing average passenger waiting time and minimizing inter-aircraft unfairness, parameterized by a priority weight (α) for aircraft type.
• Aircraft are ranked by priority (e.g., heavy planes first), and the model computes waiting time distributions based on active aircraft count and sequence.
• The model is validated using real-world data from U.S. airports and calibrated to match historical taxi-out time variability and airline departure distributions.
• Sensitivity analyses are conducted across different airline market structures (1, 5, or 10 airlines) to evaluate scalability and fairness.

Experimental results

Research questions

• RQ1 How can airlines improve departure predictability and reduce passenger waiting time during peak congestion without centralized control?
• RQ2 To what extent can intra-airline flight reordering via virtual queuing reduce average passenger waiting time?
• RQ3 What is the trade-off between minimizing average waiting time and maintaining fairness across different aircraft types?
• RQ4 How does the number of participating airlines affect the optimization potential of the CVQ system?
• RQ5 Can the CVQ concept reduce reliance on government intervention while maintaining safety and efficiency at peak capacity?

Key findings

• The CVQ reduces wheels-off time uncertainty by keeping aircraft out of physical taxiway queues, improving predictability of departure times.
• Prioritizing heavy aircraft over small ones (α = 1) can reduce average passenger waiting time by up to 15% during congestion.
• This same policy increases waiting time standard deviation by 30 seconds for medium planes and 3 minutes for small planes, indicating a fairness trade-off.
• Even with 10 airlines sharing operations (a realistic scenario at Logan Airport), a Heaviest-Plane-First policy can still reduce passenger waiting time by up to 12%.
• The CVQ enables airlines to achieve better operational objectives—such as minimizing passenger delays—while remaining within overall traffic demand constraints.
• The framework supports NextGen objectives by reducing the need for government intervention and enhancing scalability under high traffic loads.