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Open research questions in Air Traffic Management and Optimization

71 gap statements mined from Air Traffic Management and Optimization papers in our 4.5M-paper local library, which holds 643 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • Regional modeling approaches for visibility prediction are mentioned, but the scalability and computational efficiency of deploying multiple machine learning models simultaneously across a network of airports (e.g., regional aviation hub with 5-10 airports) with sub-hourly update requirements remains unspecified.

    Nowcasting of Airport Low Visibility Based on Machine Learning · 2026 · DOI

  • The hybrid model's soft-voting ensemble achieves 91.5% accuracy on the static dataset, but cross-airport and cross-airline generalization has not been validated. The XGBoost-ANN combination should be tested on geographic regions and airline carriers not represented in the training data to assess domain adaptation for flight delay prediction.

    Smart Prediction of Weather-Induced Flight Delays Applying Deep Learning · 2026 · DOI

  • The lack of common guidelines for UTM solutions. The substantial incompatibilities among existing UTM solutions. The need for a unified UTM framework.

    Bridging the Fragmentation in Unmanned Aircraft System Traffic Management (UTM): A Systematic Survey on UTM · 2026 · DOI

  • There is a critical analytical void regarding airports operating under chronic instability or active conflict. There is a lack of facility-level, empirical data showing how an airport's pre-conflict digital flaws dictate the speed and severity of its post-conflict operational collapse.

    Cyber-Resilience in Fragile States: A Retrospective Baseline Analysis of Systemic Vulnerabilities at Khartoum International Airport · 2026 · DOI

  • Naturally, this study has limitations. It relies on a single, pre-war quantitative snapshot that tracks institutional perception rather than real-time technical forensic logs during the war. Additionally, while our sample size (\(N=19\)) is highly targeted and valid for an expert-led study of a single airport, it limits our ability to broadly apply these findings to other regions.

    Cyber-Resilience in Fragile States: A Retrospective Baseline Analysis of Systemic Vulnerabilities at Khartoum International Airport · 2026 · DOI

  • Future research should focus on methods to integrate diverse data sources and facilitate a robust information exchange among various aviation systems. It should be investigated by training programs, skills development programs, and organization change programs to facilitate this transition. Another significant weakness of the existing literature is that ethical considerations are also an important limitation.

    Digital Transformation and Artificial Intelligence in Aviation Management: A Systematic Review of Emerging Trends and Business Implications · 2026 · DOI

  • The paper identifies the need for computationally efficient and accurate safety analysis.

    Improving aviation safety analysis: Automated HFACS classification using reinforcement learning with group relative policy optimization · 2026 · DOI

  • Traditional methods using HFACS are limited by scalability and consistency. The paper identifies the need for computationally efficient and accurate safety analysis.

    Improving aviation safety analysis: Automated HFACS classification using reinforcement learning with group relative policy optimization · 2026 · DOI

  • To address the industry pain points of insufficient research on flight allocation optimization for airport baggage sorting resources and low solving efficiency for large-scale problems, a bi-objective mixed-integer programming model was constructed to minimize resource occupation and total idle time.

    Modeling and algorithm solution of resource allocation for airport baggage handling system · 2026 · DOI

  • The study is limited to a specific group of 18 experts. The research is based on a literature review and expert evaluations, without empirical data.

    Hava Taşımacılığında Uçak Gecikme Nedenlerinin Çok Kriterli Karar Verme Yöntemleriyle Önceliklendirilmesi: Vikor ve Copras Yaklaşımı · 2026 · DOI

  • Further research can be conducted to validate the proposed evaluation model using empirical data. The study's findings can be applied to other industries with similar complex systems. Future research can explore the use of other multi-criteria decision-making methods in evaluating flight delay factors.

    Hava Taşımacılığında Uçak Gecikme Nedenlerinin Çok Kriterli Karar Verme Yöntemleriyle Önceliklendirilmesi: Vikor ve Copras Yaklaşımı · 2026 · DOI

  • Further research of the potential benefits of proactive planning of high-performance aircraft training operations in flexible airspace structures for pollution reduction is focused on the peculiarities of potential shifting of flight slots as a response to local variation of civil traffic demand.

    Benefits of Proactive Planning of High-Performance Aircraft Training Operations in Flexible Airspace Structures for Pollution Reduction · 2026 · DOI

  • The paper does not provide a comprehensive comparison with other methods. The computational experiments are limited to a specific dataset. The paper does not discuss the scalability of the proposed method.

    A Benders and column generation method to the integrated airline schedule and aircraft recovery with gate reassignment · 2026 · DOI

  • The takeoff-speed prediction problem has not been addressed using deep learning techniques. There is a need for accurate prediction of takeoff speed to improve flight safety.

    Aircraft takeoff speed prediction with deep learning: a comparative study of MLP, 1D-CNN, LSTM and attention-based architectures on Boeing 737-300 data · 2026 · DOI

  • The 4.9 kg gap between the XGBoost cross-validation estimate and the blind test result is consistent with standard generalization variance at this dataset scale, but no formal confidence interval over folds was reported, and the gap should not be interpreted as evidence of positive out-of-distribution behavior.

    Fuel Burn Prediction from Open Flight Trajectory and Meteorological Data for the PRC Data Challenge 2025 · 2026 · DOI

  • The studies presented here demonstrate progress in both airspace integration and urban integration; however, substantial challenges remain. One of the lessons from the case studies in Madrid, Hamburg, Belfort, Milan, and Wuhan, as well as at Purdue Airport, is that UAM implementation is highly context-dependent.

    Urban Air Mobility Solutions: UAVs for Smarter Cities · 2026 · DOI

  • This research, a collaboration involving international and national academic, industrial, and governmental entities, addresses the gap in research on UAV operations in densely populated urban areas and lays the foundation for further enhancements in UAV routing models.

    Optimizing and visualizing dynamic service performance for airspace users to enable safe and efficient urban air mobility in Dubai · 2026 · DOI

  • As current low-level solvers were insufficient to plan realistic 4D trajectories for ground movements, we developed the new Safe Interval Motion Planning (SIMP) algorithm.

    Towards next-generation airport surface movement operations using hierarchical-distributed multi-agent planning · 2026 · DOI

  • Whereas environmental effects on driver cognition have been extensively studied in ground transportation, research linking low-altitude visual environment characteristics to pilot cognitive load (CL) in urban air mobility (UAM) remains scarce.

    Effects of Low-Altitude Urban Landscapes on Pilot Cognitive Load in Urban Air Mobility: An Explainable Machine Learning Approach · 2026 · DOI

  • The modality gap between the trajectory and spoken instructions is not addressed. The framework addresses the gap by integrating controlling intent into the information processing loop.

    Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control · 2024 · DOI

  • Unmanned aerial vehicle (UAV) can work as a flying computing platform to supply computation services to users when the terrestrial infrastructure is insufficient or damaged, due to its high mobility, flexibility and controllability.

    Online Optimization in UAV-Enabled MEC System: Minimizing Long-Term Energy Consumption Under Adapting to Heterogeneous Demands · 2024 · DOI

  • Existing studies have not fully explored the complex interdependencies between airspace structure parameters and fleet operation capacity, particularly regarding how various structural components and their configurations affect UAV fleet performance.

    Parametric Analysis of Landing Capacity for UAV Fleet Operations with Specific Airspace Structures and Rule-Based Constraints · 2024 · DOI

  • Future enhancements include integration with live flight and weather data APIs for genuine real-time inference. Future enhancements include LSTM-based temporal models to capture sequential delay propagation. Future enhancements include cloud deployment on AWS or Azure for scalability.

    Smart Prediction of Weather-Induced Flight Delays Applying Deep Learning · 2026 · DOI

  • This study took a step toward presenting a systematic risk assessment approach for drone-assisted services.

    A Hierarchical Quantitative Risk Assessment Framework for Evaluating Performance and Resilience in Drone-Assisted Systems · 2026 · DOI

  • To extend the proposed method to consider other recovery phases, such as crew recovery and passenger recovery. To evaluate the effectiveness of the proposed method in a real-world setting. To develop more efficient algorithms to solve the integrated problem.

    A Benders and column generation method to the integrated airline schedule and aircraft recovery with gate reassignment · 2026 · DOI

Most-cited papers in Air Traffic Management and Optimization

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Open questions raised by papers listed on this page include: (1) There is a lack of facility-level, empirical data showing how an airport's pre-conflict digital flaws dictate the speed and severity of its post-conflict operational collapse (“Cyber-Resilience in Fragile States: A Retrospective Baseline Analysis of…”, 2026); (2) Future research should focus on methods to integrate diverse data sources and facilitate a robust information exchange among various aviation systems (“Digital Transformation and Artificial Intelligence in Aviation Management: A…”, 2026); (3) Future research can explore the use of other multi-criteria decision-making methods in evaluating flight delay factors (“Hava Taşımacılığında Uçak Gecikme Nedenlerinin Çok Kriterli Karar Verme…”, 2026); (4) There is a need for accurate prediction of takeoff speed to improve flight safety (“Aircraft takeoff speed prediction with deep learning: a comparative study of…”, 2026). 71 gap statements have been mined from Air Traffic Management and Optimization papers in our 4.5M-paper local library, which holds 643 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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