Leveraging Aircraft Transponder Signals for Measuring Aircraft Fleet Mix at Non-Towered Airports

Yang, Chuyang; Mott, John H.; Bullock, Darcy M.

doi:10.15394/ijaaa.2021.1563

Cited by 5 publications

(12 citation statements)

References 2 publications

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“…Mode A/C and Mode S aircraft transponder signals are common for civilian use. Only in Mode S signals, each aircraft is assigned a fixed ICAO 24-bit address that can match with aircraft registration in the FAA database (aircraft identity) [18]. Nevertheless, this system is currently inefficient due to the low equipage rate of the general aviation fleet with transponders (about 65% [19]).…”

Section: Literature Reviewmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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Aircraft identification in airport operations is critical to various applications, including airport planning and environmental studies. Previous research and commercially available systems heavily rely on recognizing aircraft tail numbers using text recognition. However, this approach alone does not provide accurate results in situations when the tail number visibility is reduced or obstructed. Furthermore, general aviation aircraft are harder to identify because they are small in size, and their tail numbers include substantial variations in fonts, sizes, and orientations. To tackle these issues, we propose a two-step computer vision-based aircraft identification method, first identifying the aircraft type and then recognizing the tail number in a probabilistic multi-frame-based (MFB) framework. In the first step, a convolutional neural network (CNN)-based aircraft classifier is customized to decrease the search space in the registration database. In the second step, the identification process is finalized by integrating the text recognition results into the designed probabilistic MFB framework. The proposed method achieves approximately 90% identification accuracy when tested on video data collected from three general aviation airports. This is a significant improvement compared to text recognition alone, which recognizes 67% of the individual tail number characters.INDEX TERMS Intelligent transportation systems, computer vision, airports, aircraft.

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Section: Literature Reviewmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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“…In addition to traditional Mode S SS signals, information on longitude, latitude, and heading are provided by Mode S ES signals, also known as ADS-B ( 21 ). The transponder message proportions based on operations at Terre Haute Regional Airport (ICAO code: KHUF) showed that the proportions of aircraft equipped with ADS-B- reached approximately 30% in 2020 ( 22 ).…”

Section: Estimation Of Aircraft Operationsmentioning

confidence: 99%

“…Model'' code is a common primary key to combine the aircraft reference database and the aircraft registration master database, while the element ''Engine Mfr. Model'' code is used as a common primary key to connect the engine reference database and aircraft registration master database (22). The fleet mix information corresponding to aircraft-specific operations can thus be obtained in the integrated database by referring to the unique ICAO identification code.…”

Section: Extracting Fleet MIX Informationmentioning

confidence: 99%

Developing a Cost-Effective Assessment Method for Noise Impacts at Non-Towered Airports: A Case Study at Purdue University

Yang

Mott

2022

Transportation Research Record: Journal of the Transportation R

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Aircraft noise is considered as one of the environmental impacts that most adversely affects airport development. Aircraft noise estimation tools such as the Aviation Environmental Design Tool (AEDT) are widely used by airports to mitigate local concerns for airport development. Aircraft operations and fleet mix information play important roles in aircraft performance modeling for noise prediction. However, such information is difficult to obtain at almost 85% of the 3,348 National Plan of Integrated Airport Systems (NPIAS) airports, which lack full-time air traffic control facilities and personnel. Given the increasing percentage of equipped automatic dependent surveillance-broadcast (ADS-B) aircraft in the U.S. national airspace, this paper presents a cost-effective noise estimation approach for non-towered general aviation (GA) airports. Instead of deploying physical sound meters or relying on the local air traffic control facilities, the authors collected aircraft positional data and fleet mix information based on ADS-B broadcasts. Advanced aircraft performance model parameters are constructed using four-dimensional aircraft position and a kinematic point-mass model. Aircraft-specific noise levels are interpolated based on the EUROCONTROL Aircraft Noise and Performance (ANP) database. To obtain the final noise level, two adjustments are applied to the interpolated noise values. The test results show the proposed approach can help non-towered airports predict the noise impact on any selected locations near the airport cost-effectively. Recommendations and opportunities for future investigation are also included.

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“…The method provided counts within 2.2% of actual operations during the evaluation period, but still requires the airport to obtain and later potentially maintain additional equipment. The developed transponder data system has been used for various applications such as measuring aircraft fleet mix (Yang et al, 2021) and for aircraft performance models (Yang & Mott, 2021).…”

Section: Current Counting Techniquesmentioning

confidence: 99%

A Method for Automatic Airport Operation Counts Using Crowd-Sourced Ads-B Data

Fala

Falas

2022

Aviation

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Airports are tasked with counting and reporting their operations at least yearly. The counts are used at the local and national level to schedule maintenance, for research, and to receive funds, making their accuracy important. Historically, methods for counting operations at non-towered airports have relied on additional equipment at the airport or statistical estimates. In this work, we introduce a method to use crowd-sourced Automatic Dependent Surveillance – Broadcast (ADS-B) data from the OpenSky network to automatically count airport operations and report it separated by takeoffs and landings. We use two airports as case studies – Tulsa International Airport (TUL) and Purdue University Airport (LAF) – and compare the estimated operation counts from the ADS-B data algorithm to numbers reported through the Federal Aviation Administration’s (FAA) Air Traffic Activity Data System (ATADS).

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Leveraging Aircraft Transponder Signals for Measuring Aircraft Fleet Mix at Non-Towered Airports

Cited by 5 publications

References 2 publications

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

Developing a Cost-Effective Assessment Method for Noise Impacts at Non-Towered Airports: A Case Study at Purdue University

A Method for Automatic Airport Operation Counts Using Crowd-Sourced Ads-B Data

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