The environmental impact of aviation has been the subject of significant research efforts for several decades. While reducing carbon emissions has reached a consensus across different stakeholders, leading to efforts to reduce route inefficiencies in air traffic management systems, other climate effects like contrails have spurred a different kind of discussion in the research community. Some research has rapidly moved into the pre-operational phase, aiming to reduce the climate impact of aviation by optimizing flight trajectories to avoid contrail formation. Notably, recent commercial projects from Google and Breakthrough Energy have been fast-tracking the operational perspective of contrail avoidance. In our past research, we have established a robust and fast methodology for trajectory optimization to minimize contrail formation based on TOP, a trajectory optimizer using the OpenAP aircraft performance model. In this paper, we address the practical challenges of implementing contrail-aware routing strategies in the aviation industry. We analyze the trade-offs between fuel consumption and contrail avoidance, the impact of weather forecast uncertainty on contrail mitigation strategies, the effects of contrail mitigation on airspace capacity and network operations, and the implications of contrail reduction strategies on the aviation industry and regulatory frameworks. We use a dataset of flight trajectories over Europe on a day with significant contrail potential to conduct a data-driven analysis of these challenges. Then, we demonstrate the potential difficulties in implementing contrail-optimal routing in practice, especially concerning the uncertainties in weather forecasts, airspace capacity, and the responsibility for optimal routing. Overall, we argue that contrail optimal routing should be approached with caution, and it may not be as straightforward as promoted by some stakeholders.

Accurately estimating aircraft fuel flow is critical for evaluating new procedures, designing next-generation aircraft, and monitoring the environmental impact of current aviation practices. This paper investigates the generalization capabilities of deep learning models for fuel flow prediction, focusing on their performance with aircraft types not included in the training data. We propose a novel methodology that combines neural network architectures with domain generalization techniques to improve robustness and reliability across different aircraft types. Using a comprehensive dataset of 101 aircraft types, split into training (64 types) and generalization (37 types) sets with each type represented by 1,000 flights, we introduce a pseudo-distance metric to quantify aircraft type similarity and explore sampling strategies to improve model performance in data-limited regions. Our findings show that for unseen aircraft types, especially with noise regularization, the model outperforms baselines such as corrected proxy estimates. This study demonstrates the potential of blending domain-specific insights with advanced machine learning techniques to develop scalable, accurate, and generalizable fuel flow estimation models.

The OpenSky Network has been collecting and providing crowdsourced air traffic surveillance data since 2013. The network has primarily focused on Automatic Dependent Surveillance-Broadcast (ADS-B) data, which provides highfrequency position updates over terrestrial areas. However, the ADS-B signals are limited over oceans and remote regions, where ground-based receivers are scarce. To address these coverage gaps, the OpenSky Network has begun incorporating data from the Automatic Dependent Surveillance-Contract (ADS-C) system, which uses satellite communication to track aircraft positions over oceanic regions and remote areas. In this paper, we analyze a dataset of over 720,000 ADS-C messages collected in 2024 from around 2,600 unique aircraft via the Alphasat satellite, covering Europe, Africa, and parts of the Atlantic Ocean. We present our approach to combining ADS-B and ADS-C data to construct detailed long-haul flight paths, particularly for transatlantic and African routes. Our findings demonstrate that this integration significantly improves trajectory reconstruction accuracy, allowing for better fuel consumption and emissions estimates. We illustrate how combined data captures flight patterns across previously underrepresented regions across Africa. Despite coverage limitations, this work marks an important advancement in providing open access to global flight trajectory data, enabling new research opportunities in air traffic management, environmental impact assessment, and aviation safety.

Persistent contrails significantly contribute to aviation’s climate impact through radiative forcing effects. Japanese airspace, characterized by high traffic density, prevalent short-haul flights, and diverse meteorological conditions, exhibits unique contrail formation patterns requiring tailored mitigation strategies. However, approaches such as altitude adjustments for contrail avoidance may lead to air traffic concentration at specific altitudes, raising aviation safety concerns. Therefore, this study identifies high-impact regions in Japanese airspace where contrail mitigation strategies can be effectively applied. Using the CoCiP model, CARATS Open Data, and ERA5 reanalysis, the analysis highlights critical seasonal and geographical patterns of contrail formation. Based on CARATS Open Data from 2019, which includes 399,541 flights across en route and oceanic airspace, April to June emerge as peak periods for contrail energy forcing (EF), driven by stable, humid atmospheric conditions. High-EF hotspots in southwestern, central, and northern Japan align with dense air traffic routes, with 1.71% of flights accounting for 80% of total contrail EF. A strong correlation between contrail altitude and persistence underscores the effectiveness of altitude adjustments for mitigation. Targeted strategies, such as nighttime altitude changes and interventions in high-EF sectors, could significantly reduce aviation’s climate impact. These findings establish a foundation for integrating contrail reduction measures into air traffic management systems in Japan, providing actionable insights for balancing climate benefits and operational safety.

Condensation trails, or contrails, are line-shaped clouds that are produced by an aircraft engine exhaust. These contrails often impact climate significantly due to their potential warming effect. Identification of contrail formation through satellite images has been an ongoing research challenge. Traditional computer vision techniques struggle with varying imagery conditions, and supervised machine learning approaches often require a large amount of hand-labeled images. This study researches few-shot transfer learning and provides an innovative approach for contrail segmentation with a few labeled images. The methodology leverages backbone segmentation models, which are pretrained on existing image datasets and fine-tuned using an augmented contrail-specific dataset. We also introduce a new loss function, SR loss, which enhances contrail line detection by incorporating Hough transformation in model training. This transformation improves performance over generic image segmentation loss functions. The openly shared few-shot learning library, contrail-seg, has demonstrated that few-shot learning can be effectively applied to contrail segmentation with the new loss function.

Aircraft carry additional fuel reserves, referred to as contingency fuel, used to account for unforeseen events during a flight. Previous research has attempted to quantify the magnitude of such events, most notably the probability of adverse weather or ATFM regulation, yet their inherent unpredictability introduces uncertainty and frequently results in the overestimation of contingency fuel requirements. Recent studies use data-driven fuel-burn predictions to better estimate contingency fuel sizing; however, most are confined to specific routes or regions, limiting generalizability. To address this, we utilise real operational airline data covering both regional and intercontinental flights, and develop a quantile regression framework for predicting contingency fuel requirements, capable of adapting to more diverse set of flight characteristics. Our framework integrates flight-plan data, TAF weather forecasts, and proxy congestion features to predict required contingency fuel at varying quantile levels, enabling trade-offs between efficiency and safety. Unlike the current Statistical Contingency Fuel process, which applies different coverage levels by risk category, this evaluation uses a single fixed quantile for all flights when generating predictions. In a four-month out-of-sample evaluation, a single fixed quantile matched the safety performance of the Statistical Contingency Fuel process while reducing excess fuel carriage by up to 235,364 kg (≈11%). A more conservative quantile configuration yielded smaller savings but reduced abnormal flight-phase events by 22.2%. The key drivers of the final predictions are evaluated, offering pilots and dispatchers transparent explanations that can build trust and reduce reliance on discretionary fuel loading.

The mass of an aircraft is crucial for performance-related studies, such as predicting flight trajectories and analyzing flight emissions. In these studies, the flight trajectories are often reconstructed using a point-mass aircraft performance model combined with flight profiles from surveillance data and take-off mass information. However, airlines do not usually disclose take-off mass information, considering its sensitive nature. Thus, aircraft masses often need to be assumed or estimated. This paper presents a simple and computationally effective approach for estimating take-off mass using only open data and models. We explore the strong correlation between take-off mass, flight distance, cruise altitude, and partially, the airspeed during the cruise. The main idea is to generate fuel-optimal trajectories with known masses and distances, and then compare them with actual flight data. The optimal trajectories are generated using the open aircraft performance and optimization library. By assuming that actual flights follow quasi-fuel-optimal trajectories, the take-off mass of a flight can be estimated based on simple regression models trained on the optimal trajectory dataset. This open-loop take-off mass estimation approach requires no proprietary information from aircraft manufacturers or airlines. We verified the model with an anonymized dataset containing actual A320 flights with known take-off mass. Our two- and three-feature multi-linear models yield mean absolute percentage errors of 5.95 % and 4.89 %, respectively. This study is another step forward in open science and a contribution to the aircraft trajectory studies.

Radiotelephony remains the primary medium for pilot-controller communication, yet extracting structured information from spoken exchanges is challenging. Deep learning approaches often depend on large annotated datasets, limiting use in data-scarce environments. This study evaluates open-source Large Language Models for Structured Information Extraction from ATC communications, with applications in assisting or automating pseudo-pilot tasks. We evaluate Llama 3.3 (70B) with baseline prompting and Gemma 3 (4B) with baseline and fine-tuned variants on 496 utterances from NLR’s ATM simulator: NARSIM (NLR ATC real-time simulator). Performance is assessed on human transcripts and ASR outputs from Whisper models, with varying prompt contexts. Cross-sector generalization is tested across two ATC sectors. Using manual scoring, Llama 3.3 achieves micro-F1 0.95 on human transcripts and 0.86 on fine-tuned Whisper outputs. While Gemma 3 performed weaker in its baseline form, fine-tuning on a small sample led to notable improvements. Results demonstrate the potential of LLMs for ATC applications without the need for large annotated datasets.

Punctuality is a key performance indicator for any airline, especially hub-and-spoke airlines, given their focus on short passenger connections. Flights that are delayed at departure need to compensate for lost time whilst airborne. Because fuelling takes place well before scheduled departure, predicted departure delays determine the planned fuel amounts for en-route speed optimization. To prevent unnecessary fuel burn, airlines benefit from highly accurate departure delay predictions. This study aims to extend previous work on airline departure delay forecasting to a dynamic and probabilistic domain, whilst incorporating novel day-of-operations airline information to further minimize prediction errors. Random Forest, CatBoost, and Deep Neural Network models are proposed for a case study on departure flights of a major hub-and-spoke airline from its hub airport between 1 January 2020 and 1 August 2023. The Random Forest model is selected for its probabilistic performance and high accuracy in predicting delays between 5 and 25 min, for which en-route speed optimization has the largest effect. At the 90 min prediction horizon, the model reaches a Mean Absolute Error of 8.46 min and a Root Mean Square Error of 11.91 min. For 76% of flights, the actual delay is within the predicted probability distribution range. Finally, this study puts a strong emphasis on explainability. Flight dispatchers are therefore provided with the main factors impacting the prediction, explaining the context of the flight. The versatility of the model is demonstrated in two shadow runs within the procedures of an international airline, where delays caused by familiar and unfamiliar factors were successfully predicted.

tangram is an open research framework for real-time processing of high-throughput geospatial surveillance streams, with a primary application in ADS-B and Mode S surveillance data. While large-scale historical datasets have motivated extensive aviation research, the transfer of methods and algorithms to live data streams remains less documented. This transfer is significantly more challenging: real-time analyses are difficult to implement, debug, and reproduce. tangram addresses this gap by providing a modular and extensible platform that lowers the technical barriers for researchers, enabling them to integrate their own algorithms without developing a full streaming infrastructure, so that they can focus on addressing their own research questions. This paper introduces the architecture of the platform and demonstrates its flexibility through four representative use cases in air traffic management: integrating weather forecasts, estimating fuel flow, analysing contrail formation, and monitoring airport performance. Together, these examples illustrate how tangram enables reproducible, real-time experimentation and opens new perspectives for operationally relevant research in aviation.

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.

Contrail optimization offers an efficient and cost-effective way for aviation to immediately reduce its climate impact. Open-source optimization, wherein the contrail and emission effects are balanced based on meteorological open data, has been presented in previous work. However, prior research overlooks the importance of using forecasting data, as opposed to post-processed reanalysis data. For contrail optimization to be implementable, forecasting data needs to be available at a sufficient quality in the flight planning stage in order to perform the optimization. In this paper, a fully open non-linear optimal control flight optimization is implemented and applied using both forecasting and reanalysis data. A total of 120 days (175.440 flights) of flight data from OpenSky are used in the analysis. We show that forecasts with larger lookahead times (up to 12 hours) are equally effective when compared to more recent forecasts (1 hour lookahead time) for contrail optimization, with equally high accuracy. However, when compared to more accurate post-processed reanalysis data, there are considerable differences in predicted contrails formed. This research shows there is still a long way to go before we can actually implement contrail optimal flight planning.