Technology Assessment to Improve Operations Counts at Non-Towered Airports

Yang, Chuyang; Mott, John H.; Hardin, Brandon; Zehr, Steven; Bullock, Darcy M.

doi:10.1177/0361198119835804

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…Nevertheless, this system is currently inefficient due to the low equipage rate of the general aviation fleet with transponders (about 65% [19]). In addition, many of the general aviation aircraft in the U.S. (approximately 84% [20]) do not have transponders capable of transmitting Mode S signal that contains aircraft identity information. Consequently, this system cannot identify even a large portion of the equipped aviation fleet.…”

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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“…Mott et al ( 15 ) developed an assessment approach for measuring aircraft operations using aircraft transponder devices that increased the accuracy of operations identification. This approach was further tested and validated as a cost-effective technology to estimate operations at non-towered airports ( 16 ).…”

Section: Related Workmentioning

confidence: 99%

“… Note : KLAF = International Civil Aviation Organization (ICAO) code of Purdue University Airport; KTYQ = ICAO code of Indianapolis Executive Airport; KHUF = ICAO code of Terre Haute Regional Airport. Source: ACRP Report 129 ( 13 ). Source from “Technology Assessment to Improve Operations Counts at Non-Towered Airports” ( 16 ). …”

Section: Related Workmentioning

confidence: 99%

“…In this study, the proposed model uses ADS-B signals to estimate aircraft operations and extract fleet mix information. The operations estimation process was developed by Mott and Bullock ( 21 ) and was further validated and improved by Yang et al ( 16 ) and Mott et al ( 23 , 24 ). In Mott’s algorithms, aircraft operation counts were estimated based on the determination of aircraft operation within the defined time duration.…”

Section: Estimation Of Aircraft Operationsmentioning

confidence: 99%

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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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“…Mott utilized this technology and Mode C transponders to count GA aircraft operations, but this counting method relies on cooperative aircraft and suffers from the current low equipage rate of the GA fleet with ADS-B out ( 13 – 15 ). Furthermore, even though Mode C transponders are the prevalent type of transponders, not all aircraft have Mode C transponders ( 16 ). Acoustics are also employed to collect aircraft operations ( 17 ).…”

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confidence: 99%

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

Farhadmanesh

Marković

Rashidi

2022

Transportation Research Record: Journal of the Transportation R

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The vast majority of U.S. airports are not equipped with control towers, which limits their ability to keep records of flight operations. Attempts have been made to use sensor-based technologies to count aircraft operations at non-towered airports; however, they exhibit limited accuracy. To this end, we developed an automated video-based surveillance system capable of detecting general aviation aircraft departure and landing operations, which comprise the vast majority of operations at non-towered airports. The proposed computer vision method is comprised of three modules: aircraft detection, aircraft tracking, and operations count and classification. We explored different camera layouts and state-of-the-art machine learning and deep learning methods to determine the best settings to extract operations trajectory features for operations count and classification. The proposed method was tested at five non-towered airports. Integrating deep-neural-network-based aircraft detectors and image-correlation-based aircraft trackers achieved an accuracy of about 95%, while ensuring processing times that are needed for real-time implementation.

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Technology Assessment to Improve Operations Counts at Non-Towered Airports

Cited by 9 publications

References 8 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

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

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