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.

Tribal governments bear an uneven burden in the face of escalating disaster risks, climate change, and environmental degradation, primarily due to their deeply entrenched ties to the environment and its resources. Regrettably, accessing vital information and evidence to secure adequate funding or support poses difficulties for enrolled tribal members and their lands. In response to these challenges, this article collaborates with tribal nations to co-design intelligent disaster management systems using AI chatbots and drone technologies. The primary objective is to explore how tribal governments perceive and experience these emerging technologies in the realm of disaster reporting practices. This article presents participatory design studies. First, we interviewed seasoned first-line emergency managers and hosted an in-person design workshop to introduce the Emergency Reporter chatbot. Second, we organized a follow-up design workshop on tribal land to deliberate the utilization of drones within their community. Through qualitative analysis, we unveiled key themes surrounding integrating these emergency technologies within the jurisdiction of tribal governments. The findings disclosed substantial backing from tribal governments and their tribal members for the proposed technologies. Moreover, we delved into the potential of chatbots and drones to empower tribal governments in disaster management, safeguard their sovereignty, and facilitate collaboration with other agencies. ¹

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.

Realizing the benefits of drones while minimizing public concerns requires development and implementation of drone use policies that are grounded in an understanding of drone users and their behavior. This study aims to contribute to data-driven smart cities by filling our gap in knowledge about city drone users and their compliance behavior. The literature review has identified the main factors affecting drone policy compliance. This study collects data via a national survey of adults on drone behavior and focuses on city drone users. The results show that city drone users are younger with more dispersed educational backgrounds and income distribution than those in the general population. Moreover, civic duty, trust in government, and knowledge about regulatory requirements are motivators for drone users to comply with drone regulation.