Analysis of Airport Performance using Surface Surveillance Data: A Case Study of BOS

Khadilkar, Harshad; Balakrishnan, Hamsa; Reilly, Brendan

doi:10.2514/6.2011-6986

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…At Boston Logon International Airport, Simaiakis et.al. [3][4] field tested a concept for metering the number of flights allowed to push back from the gate onto the airport taxiway. Results showed that the concept was able to save 3,900-4,900 US gallons of fuel during four eight-hour tests by decreasing the taxi time, with a increase in gate departure delay of 4.3 minutes per flight on average.…”

Section: Introductionmentioning

confidence: 99%

Towards a Fast-time Simulation Analysis of Benefits of the Spot and Runway Departure Advisor

Windhorst

2012

AIAA Guidance, Navigation, and Control Conference

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NASA Ames Research Center is developing a concept for managing flight operations on airport surfaces. The goal of the concept, named the Spot and Runway Departure Advisor (SARDA), is to reduce delays, emissions, noise, and fuel consumption. In 2010, human-inthe-loop simulations of the SARDA concept were conducted. 1 Results showed that for the 2008 heavy traffic scenario SARDA reduced departure delays and fuel consumption, while imposing little impact on perceived controller workload. The 2008 normal traffic scenario did not show measurable benefits. The human-in-the-loop simulations analyzed only two traffic scenarios and were costly. To efficiently expand the research of SARDA so that it included analysis of more traffic scenarios, adaptation to more airports, and investigation of changes to the concept, a fast-time simulation capability was needed. This paper documents the first attempt at fast-time simulation analysis of the SARDA concept. A fast-time simulation of traffic at Dallas/Fort Worth International Airport being autonomously managed by a delay-optimal runway scheduler was conducted. Although the simulation was meant to analyze the benefits of the SARDA concept, there were differences between how traffic was managed in the fast-time simulation and the human-in-the-loop simulations. The differences were due to difficulties adapting the delay-optimal scheduler from the human-inthe-loop simulations to the fast-time simulation and lack of capabilities of the airport operations model. The main differences were that the human-in-the-loop simulations included runway crossings in the optimal departure schedules, while the fast-time simulation did not, and the human-in-the-loop simulations controlled flights at the spot, while the fasttime simulation controlled flights at the gate. Results of the delay-optimal fast-time simulation showed benefits for the 2008 normal traffic schedule. It produced 8% less average taxi time and 40% fewer stops than a simulation controlled by a first-come-firstserved scheduler. However, the delay-optimal simulation had the same average delays as the first-come-first-served simulation. In terms of equity of flight delays, the worst delayed flight in the delay-optimal simulation had one and a half minutes more delay than the worst delayed flight in the first-come-first-served simulation.

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Section: Introductionmentioning

confidence: 99%

Towards a Fast-time Simulation Analysis of Benefits of the Spot and Runway Departure Advisor

Windhorst

2012

AIAA Guidance, Navigation, and Control Conference

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“…In particular, the data can be used to characterize surface flows, including identification of congestion hotspots, queue dynamics and departure throughput, and to develop metrics to evaluate the daily and long-term operational performance of an airport under different operating conditions. These metrics can provide useful feedback on operational performance to airport operators, and therefore have the potential to improve the efficiency of surface operations at airports [42,45].…”

Section: Metrics To Characterize Airport Operational Performancementioning

confidence: 99%

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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“…Aircraft wheels-off times are typically captured with a precision of seconds. In addition, ASDE-X data can be used to measure the precise number of aircraft that are physically present in the queuing area at the departure runway threshold [10,11]. As a result, runway inter-departure times can be measured conditioned on the actual state of the departure queue.…”

Section: Data Sourcesmentioning

confidence: 99%

Probabilistic Modeling of Runway Interdeparture Times

Simaiakis

Balakrishnan

2014

Journal of Guidance, Control, and Dynamics

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Abstract. This paper examines the validity of the Erlang distribution for runway service times. It uses high-fidelity surface surveillance data, for the first time, to model the probability distributions of runway service times and departure throughput, and to validate the Erlang service time assumption. The paper proposes several potential approaches to determine departure runway service time distributions from empirical data, and compares the results. In particular, it finds that a displaced exponential fit may be a better match to the empirical service time distribution than an Erlang distribution. However, the computational benefits offered by the Erlang service time distribution, its accurate reflection of the means and variances of the empirical service time and throughput distributions, and its ability to represent the tail of the service time distribution, make it attractive for use in queuing models of airport operations.

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Analysis of Airport Performance using Surface Surveillance Data: A Case Study of BOS

Cited by 7 publications

References 9 publications

Towards a Fast-time Simulation Analysis of Benefits of the Spot and Runway Departure Advisor

Towards a Fast-time Simulation Analysis of Benefits of the Spot and Runway Departure Advisor

Challenges in Aerospace Decision and Control: Air Transportation Systems

Probabilistic Modeling of Runway Interdeparture Times

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