Junzi Sun

Justas Andriuškevičius, Junzi Sun

Recent developments in language models have created new opportunities in air traffic control studies. The current focus is primarily on text and language-based use cases. However, these language models may offer a higher potential impact in the air traffic control domain, thanks to their ability to interact with air traffic environments in an embodied agent form. They also provide a language-like reasoning capability to explain their decisions, which has been a significant roadblock for the implementation of automatic air traffic control. This paper investigates the application of a language model-based agent with function-calling and learning capabilities to resolve air traffic conflicts without human intervention. The main components of this research are foundational large language models, tools that allow the agent to interact with the simulator, and a new concept, the experience library. An innovative part of this research, the experience library, is a vector database that stores synthesized knowledge that agents have learned from interactions with the simulations and language models. To evaluate the performance of our language model-based agent, both open-source and closed-source models were tested. The results of our study reveal significant differences in performance across various configurations of the language model-based agents. The best-performing configuration was able to solve almost all 120 but one imminent conflict scenarios, including up to four aircraft at the same time. Most importantly, the agents are able to provide human-level text explanations on traffic situations and conflict resolution strategies.

Junzi Sun, Esther Roosenbrand

Air transport poses significant environmental challenges, particularly regarding the role of flight contrails in climate change due to their potential global warming impact. Traditional computer vision techniques struggle under varying remote sensing image conditions, and conventional machine learning approaches using convolutional neural networks are limited by the scarcity of hand-labeled contrail datasets. To address these issues, we employ few-shot transfer learning to introduce an innovative approach for accurate contrail segmentation with minimal labeled data. Our methodology leverages backbone segmentation models pre-trained on extensive image datasets and fine-tuned using an augmented contrail-specific dataset. We also introduce a novel loss function, termed SR Loss, which enhances contrail line detection by transforming the image space into Hough space. This transformation results in a significant performance improvement over generic image segmentation loss functions. Our approach offers a robust solution to the challenges posed by limited labeled data and significantly advances the state of contrail detection models.

Gabriel Jarry, Ramon Dalmau, Philippe Very et al.

Accurate modelling of aircraft fuel-flow is crucial for both operational planning and environmental impact assessment, yet standard parametric models often neglect performance deterioration that occurs as aircraft age. This paper investigates multiple approaches to integrate engine ageing effects into fuel-flow prediction for the Airbus A320-214, using a comprehensive dataset of approximately nineteen thousand Quick Access Recorder flights from nine distinct airframes with varying years in service. We systematically evaluate classical physics-based models, empirical correction coefficients, and data-driven neural network architectures that incorporate age either as an input feature or as an explicit multiplicative bias. Results demonstrate that while baseline models consistently underestimate fuel consumption for older aircraft, the use of age-dependent correction factors and neural models substantially reduces bias and improves prediction accuracy. Nevertheless, limitations arise from the small number of airframes and the lack of detailed maintenance event records, which constrain the representativeness and generalization of age-based corrections. This study emphasizes the importance of accounting for the effects of ageing in parametric and machine learning frameworks to improve the reliability of operational and environmental assessments. The study also highlights the need for more diverse datasets that can capture the complexity of real-world engine deterioration.