Integrated Surface–Airspace Model of Airport Departures

Badrinath, Sandeep; Li, Max Z.; Balakrishnan, Hamsa

doi:10.2514/1.g003964

Cited by 17 publications

(17 citation statements)

References 20 publications

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“…These figures show a good match between the predictions and observed values. A similar match is also observed for CLT[20]. Average taxi-out time (July 10, 2017) (c) Taxi-out time distribution (14,100 flights) Comparison between model predictions and data for CDG.…”

supporting

confidence: 71%

“…The queue length can be predicted by integrating the dynamics forward in time with appropriate server parameters and pushback rate. The wait times of aircraft entering the queue are determined using the predictions of queue length and time-varying mean service rates [20]. The taxi-out time is then determined as the sum of the unimpeded gate-to-runway time plus the waiting time in the queue.…”

Section: Queuing Network Model Of Cdgmentioning

confidence: 99%

“…The arrival traffic flow from the spot to the gates impedes the departure movement in the ramp area. Therefore, the service time distribution of the taxi-out ramp server is modelled as a function of the length of the taxi-in ramp queue, and vice versa [20]. The impact of fleet mix was not explicitly considered in the queue model because only a small percentage of aircraft at CLT (2%) belong to the 'heavy' category of aircraft weight class.…”

Section: Service Time Distributionsmentioning

confidence: 99%

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Comparative Analysis of Departure Metering at United States and European Airports

Badrinath¹,

Balakrishnan²,

Ma³

et al. 2020

Journal of Air Transportation

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Departure metering has the potential to mitigate airport surface congestion and decrease flight delays. This paper considers several candidate departure metering techniques, including a trajectory-based optimization approach using a node-link model and three aggregate queuebased approaches (a scheduler meant to represent NASA's Airspace Technology Demonstration-2 (ATD-2) logic, an optimal control approach, and a robust control approach). The outcomes of these different approaches are compared for two major airports: Paris Charles De Gaulle airport (CDG) in Europe and Charlotte Douglas International airport (CLT) in the United

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supporting

confidence: 71%

Section: Queuing Network Model Of Cdgmentioning

confidence: 99%

Section: Service Time Distributionsmentioning

confidence: 99%

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Comparative Analysis of Departure Metering at United States and European Airports

Badrinath¹,

Balakrishnan²,

Ma³

et al. 2020

Journal of Air Transportation

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“…The taxi-in process in CLT-NF is represented as follows: flights landing on the leftmost runway (36L) pass through a runway crossing queue and a taxi-in ramp queue, whereas flights landing on one of the other runways just pass through the taxi-in ramp queue. The service rate of the taxi-out ramp server is modeled as a function of the taxi-in ramp queue length, and vice versa (Badrinath, Li, and Balakrishnan 2018). The three queuing network models (EWR-SF, DFW-SF and CLT-NF) correspond to a progression of complexity, going from a single runway queue to two runway queues, and finally multiple runway and ramp queues.…”

Section: Queuing Network Models Of Ewr Dfw and Cltmentioning

confidence: 99%

“…In the US, airlines are now encouraged to publish EOBT information for their flights, and they are expected to dynamically update each of them until the flight pushes back from the gate (FAA Surface CDM Team 2012). The dynamic updates are intended to make the EOBT more complex queuing network models may be needed for airports that have several points of congestion or multiple departure runways (Badrinath, Li, and Balakrishnan 2018).…”

Section: Introductionmentioning

confidence: 99%

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

Badrinath

Balakrishnan

Joback

et al. 2020

Transportation Science

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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.

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Data-driven trajectory-based analysis and optimization of airport surface movement

Zhou

Liang

et al. 2022

Transportation Research Part C: Emerging Technologies

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Integrated Surface–Airspace Model of Airport Departures

Cited by 17 publications

References 20 publications

Comparative Analysis of Departure Metering at United States and European Airports

Comparative Analysis of Departure Metering at United States and European Airports

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

Data-driven trajectory-based analysis and optimization of airport surface movement

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