Software tools to support research on airport departure planning

Carr, Francis; Evans, A.; Féron, Éric; Clarke, John‐Paul

doi:10.1109/dasc.2002.1067900

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Model the dependence of τ taxiway on R(t), given RC and M C. 3. Model the statistical characteristics of the runway service process given RC and M C. Then, given a pushback schedule and gate locations, we can use Equations (3)(4)(5) to get the outputs of the models.…”

Section: Model Structurementioning

confidence: 99%

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Queuing Models of Airport Departure Processes for Emissions Reduction

Simaiakis

Balakrishnan

2009

AIAA Guidance, Navigation, and Control Conference

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Aircraft taxiing on the surface contribute significantly to the fuel burn and emissions at airports. This paper investigates the possibility of reducing fuel burn and emissions from surface operations through a reduction of the taxi times of departing aircraft. A novel approach is proposed that models the aircraft departure process as a queuing system, and attempts to reduce taxi times and emissions through improved queue management strategies.The departure taxi (taxi-out) time of an aircraft is represented as a sum of three components, namely, the unimpeded taxi-out time, the time spent in the departure queue, and the congestion delay due to ramp and taxiway interactions. The dependence of the taxi-out time on these factors is analyzed and modeled. The performance of the model is validated through a comparison of its predictions with observed data at Boston's Logan International Airport (BOS). The reductions in taxi-out times from the proposed queue management strategy are translated to reductions in fuel burn and emissions using ICAO engine models for the taxi phase of the flight profile. Nomenclature P SPushback schedule RC Runway configuration M C Meteorological conditions GL Gate location of a departing flight P (t) Number of aircraft pushing back during time period t N (t) Number of departing aircraft on the surface at the beginning of period t Q(t)Number of aircraft waiting in the departure queue at the beginning of period t R(t)Number of departures on the surface not at the departure queue at the beginning of period t C(t)Departure capacity of the departure runways during period t T (t) the number of takeoffs during period t N Q (i) Number of takeoffs between the pushback time and takeoff time of aircraft i τ (i)Taxi time of departing aircraft i τ unimped (i) Unimpeded taxi-out time of aircraft i τ taxiway (i) Delay to aircraft i due to aircraft interactions on the ramp and the taxiways τ dep.queue (i) Time aircraft i spends in the departure queue N * Saturation point for a segment N cntrlCritical N at which aircraft are held in the virtual departure queue

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Section: Model Structurementioning

confidence: 99%

“…3,6,21 We use the models developed in this paper to evaluate in detail the potential benefits of the strategy initially studied by Pujet et al 20 The proposed algorithm can be thought of as virtual departure queuing and is often referred to as N-Control . 4,6 It can be summarized as follows: At each time period t,…”

Section: Management Of the Pushback Queuementioning

confidence: 99%

Queuing Models of Airport Departure Processes for Emissions Reduction

Simaiakis

Balakrishnan

2009

AIAA Guidance, Navigation, and Control Conference

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“…A state-dependent pushback policy, such as the N-Control strategy [12,15,16,71], is based on the typical variation of departure throughput with the number of departures on the surface (denoted N ): As more aircraft pushback from their gates onto the taxiways, the throughput of the departure runway initially increases, as seen in Fig. 4.4 and first observed by Shumsky [82].…”

Section: Congestion Mitigation Strategiesmentioning

confidence: 95%

Challenges in Aerospace Decision and Control: Air Transportation Systems

Balakrishnan

Clarke

Féron

et al. 2015

Advances in Control System Technology for Aerospace Applications

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The Next Generation Air Transportation System (NextGen) is the FAA's vision of how the nation's aviation system will operate in 2025 and beyond [40]. In 2004, the National Airspace System (NAS) was already operating near capacity, and demand was expected to grow two-to threefold over the following 20 years. The NextGen initiative was established in 2003 in order to meet the challenges presented by the predicted increase in demand. It represents a substantial and long-term change in the management and operation of the US air transportation system, and involves both the leveraging of existing technologies and the development of new ones. This includes satellite-based navigation and control of aircraft, advanced digital communications, advanced infrastructure for greater information sharing, and enhanced connectivity between all components of the air transportation system. The overarching objectives of NextGen are to improve the safety, speed and efficiency, and to mitigate the environmental impacts of air transportation, while accommodating increased demand.

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“…The resultant N-Control strategy was a threshold heuristic, where if the total number of departing aircraft on the ground exceeded a certain threshold, N ctrl , any further aircraft requesting pushback 125 were held at their gates until the number of departures on the ground fell below the threshold [19,11]. Other variants and extensions of this policy have also been studied [20,21,22,14]. Interestingly, a similar heuristic has been known to be deployed by 130 Air Traffic Controllers at BOS during times of extreme congestion [23].…”

Section: Congestion Management Strategiesmentioning

confidence: 99%

Control and optimization algorithms for air transportation systems

Balakrishnan

2016

Annual Reviews in Control

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Software tools to support research on airport departure planning

Cited by 5 publications

References 7 publications

Queuing Models of Airport Departure Processes for Emissions Reduction

Queuing Models of Airport Departure Processes for Emissions Reduction

Challenges in Aerospace Decision and Control: Air Transportation Systems

Control and optimization algorithms for air transportation systems

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