Advanced digital data-driven applications have evolved and significantly impacted the transportation sector in recent years. This systematic review examines natural language processing (NLP) approaches applied to aviation safety-related domains. The authors use Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to conduct this review, and three databases (Web of Science, Scopus, and Transportation Research International Documentation) are screened. Academic articles from the period 2010–2022 are reviewed after applying two rounds of filtering criteria. The sub-domains, including aviation incident/accident reports analysis and air traffic control (ATC) communications, are investigated. The specific NLP approaches, related machine learning algorithms, additional causality models, and the corresponding performance are identified and summarized. In addition, the challenges and limitations of current NLP applications in aviation, such as ambiguity, limited training data, lack of multilingual support, are discussed. Finally, this review uncovers future opportunities to leverage NLP models to facilitate the safety and efficiency of the aviation system.

The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role.

Accurate airport operations counts are important for determining appropriate funding allocations for airport development and improvement. Fewer than 270 of the 2,950 non-primary airports in the United States, however, have air traffic control (ATC) personnel who are available to count airport operations. Existing counting methods such as automatic acoustic counters (AAC) are not viable long-term solutions because of the expense and inconvenience of deploying the devices on a large scale. This paper validates a cost-effective counting technology based on a technique that uses signal strength obtained from aircraft transponders to register the occurrence of aircraft operations at non-towered airports. Over 50 million transponder records were collected from two different versions of the system, which were installed at Purdue University Airport (KLAF), Terre Haute Regional Airport (KHUF), and Indianapolis Executive Airport (KTYQ), all in Indiana. The operations counts calculated from these records were compared with those obtained from the Federal Aviation Authority (FAA)’s Air Traffic Activity Data System (ATADS) database, which contains official operations data reported by airports with ATC towers. The Version I device utilized a Raspberry Pi platform and produced error rates ranging from −10.2% to +7.6%. The Version II device consisted of the pre-production commercialized system and resulted in error rates ranging from −4.9% to −1.4%. The test results suggest that this pre-production implementation of the transponder signal-counting technology is an accurate and cost-effective way to count non-towered airport operations. Improvement and testing of this technology is being undertaken, and field deployments are ongoing at additional airports.

Unmanned aerial systems (UAS), commonly known as drones, have gained widespread use due to their affordability and versatility across various domains, including military, commercial, and recreational sectors. Applications such as remote sensing, aerial imaging, agriculture, firefighting, search and rescue, infrastructure inspection, and public safety have extensively adopted this technology. However, environmental impacts, particularly noise, have raised concerns among the public and local communities. Unlike traditional crewed aircraft, drones typically operate in low-altitude airspace (below 400 feet or 122 m), making their noise impact more significant when they are closer to houses, people, and livestock. Numerous studies have explored methods for monitoring, assessing, and predicting the noise footprint of drones. This study employs a bibliometric analysis of relevant scholarly works in the Web of Science Core Collection, published from 2015 to 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) data collection and screening procedures. The International Journal of Environmental Research and Public Health, Aerospace Science and Technology, and the Journal of the Acoustical Society of America are the top three preferred outlets for publications in this area. This review unveils trends, topics, key authors and institutions, and national contributions in the field through co-authorship analysis, co-citation analysis, and other statistical methods. By addressing the identified challenges, leveraging emerging technologies, and fostering collaborations, the field can move towards more effective noise abatement strategies, ultimately contributing to the broader acceptance and sustainable integration of UASs into various aspects of society.

Aircraft performance modeling is critical for the assessment of safety and sustainability in general aviation. The Federal Aviation Administration (FAA) developed the Airport Environmental Design Tool, which uses flight data from radar and flight plans to model aircraft performance in space and time to further assess fuel consumption, emissions, noise, and air quality consequences. However, over 90% of 3,331 U.S. airports under the National Plan of Integrated Airport System (NPIAS) have limited access to air traffic data due to lack of air traffic control (ATC) facilities. Several methods have been developed and tested to capture aircraft activities at airports lacking fulltime personnel, such as video- and image-based detection, acoustic counter, statistical sampling, transponder signals-based method, etc. Most of these methods cannot be deployed on a large scale due either to high variation of accuracy or environmental/cost restrictions. The transponder signal-based method was validated as an accurate and reliable estimation of aircraft operations. Hence, this paper proposes a method to model aircraft performance using low-cost transponder receivers. First, ADS-B signals collected from aircraft are processed with a self-developed algorithm, and a 24-bit International Civil Aviation Organization (ICAO) code is extracted and applied to FAA databases to obtain fleet mix information such as aircraft type and engine type. Heuristics are then applied to model aircraft performance based on decoded parameters from ADS-B data. Data collected from Purdue University Airport (FAA code: LAF) were examined and analyzed using the proposed method, which will provide a cost-effective aircraft performance model for airport safety and sustainability assessment.

Mitigation of aircraft noise pollution is a core goal of managing environmental interactions and inherently aligns with the aim of reducing aviation emissions. As noise pollution adversely affects the operation and expansion of airports by restricting land zoning and flight patterns, developing precise noise generation and propagation models is imperative. Efforts to minimize the impact of aircraft noise are influenced by the accurate mapping of the geometric distribution of that noise. The Federal Aviation Administration (FAA) utilizes the Aviation Environmental Design Tool (AEDT) to model aircraft noise, emissions, and air quality consequences for regulatory compliance and system planning. Aircraft operations and fleet mix data are required when users execute AEDT to compute the noise exposure level. However, such data are difficult to obtain from non-towered airports that lack full-time air traffic facilities and personnel. Several airport operations estimation approaches developed by researchers have shown limitations in accuracy and cost-efficiency of deployment when tested, limiting their usefulness with regards to noise modeling. Meanwhile, tracking aircraft through onboard transponder data was validated as a cost-effective approach to estimate aircraft operations at non-towered airports.The authors designed a platform consisting of three modules. Flight operations and aircraft performance parameters can be estimated in the first module by deploying inexpensive hardware to collect aircraft transponder signals. The second module estimates aircraft noise levels by integrating information sources with the noise vs. power vs. distance (NPD) data from the EUROCONTROL Aircraft Noise and Performance (ANP) database. The third module visualizes airport noise impact based on a Geographic Information System (GIS) platform. Additionally, a risk assessment and a sustainability analysis are presented in this paper.