A Multi-Modal Unscented Kalman Filter for Inference of Aircraft Position and Taxi Mode from Surface Surveillance Data

Air Traffic Control Quarterly

2013

Self Cite

Detailed surface surveillance datasets from sources such as the Airport Surface Detection Equipment, Model-X (ASDE-X) have the potential to be used for the analysis of airport operations, in addition to their primary purpose of enhancing safety. This paper describes how surface surveillance data can be used to measure airport performance characteristics in three different ways: (1) Characterization of surface flows, including identification of congestion hotspots, queue dynamics and departure throughput; (2) Development of metrics to evaluate the daily operational performance; and (3) Development of metrics to gauge long-term performance across different runway configurations and operating conditions. The proposed metrics have been developed with active feedback from operations personnel at Boston Logan International Airport, and are therefore evaluated and discussed using this airport as an example. 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.

Section: Ic Data Analysis Methodsmentioning

confidence: 99%

Metrics to Characterize Airport Operational Performance Using Surface Surveillance Data

Air Traffic Control Quarterly

2013

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“…A total of 24,636 departing aircraft were included, and the raw surface tracks were processed using a multimodal unscented Kalman filter developed in prior work. 22 Additional validation of the identified model parameters was conducted using ASDE-X data from February-August 2011.…”

Section: Modeling Of Taxi-out Timesmentioning

confidence: 99%

Network Congestion Control of Airport Surface Operations

Journal of Guidance, Control, and Dynamics

2014

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This paper presents a novel approach to managing the aircraft taxi-out process at airports, by posing the problem in a network congestion control framework. A network model of the airport surface with stochastic link travel times is first developed. The model parameters for Boston Logan International Airport are estimated using empirical data from a surface surveillance system. Using the entry times of aircraft into the network as the control variables, a control algorithm that aims to reduce taxi-out times is proposed. This algorithm regulates network congestion while limiting the adverse effect on throughput performance. Performance characteristics of the proposed control algorithm are derived using simplified theoretical analysis, and are shown to agree with simulations of traffic on a network model of Boston Logan International Airport.

“…We do this using a multi-modal unscented Kalman filter that produces estimates of aircraft position, velocity and heading. 11 These filtered quantities are then used to tag each valid flight track with a departure/arrival time and runway. By tracking each aircraft from pushback to wheels-off, various airport states such as the active runway configuration, location and size of the departure queue, and departure/arrival counts are measured.…”

Section: B Data Analysis Methodsmentioning

confidence: 99%

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

11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference

Reilly

2011

Self Cite

Detailed surface surveillance datasets from sources such as the Airport Surface Detection Equipment, Model-X (ASDE-X) have the potential to be used for analysis of airport operations, in addition to their primary purpose of enhancing safety. In this paper, we describe how airport performance characteristics such as departure queue dynamics and throughput can be analyzed using surface surveillance data. We also propose and evaluate several metrics to measure the daily operational performance of an airport, and present them for the specific case of Boston Logan International Airport.