Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions

• URL: http://arxiv.org/abs/2605.23696v1
• Date: Fri, 22 May 2026 14:50:34 GMT
• Title: Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
• Abstract summary: Group supervisors use tools that estimate upcoming traffic load to aid decision-making.<n>This study presents a probabilistic approach to forecast the complexity of an airspace sector.<n>We adapted an existing filter algorithm to make it suitable for use in London Middle Sector.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effectively managing Air Traffic Control Officer (ATCO) workload is crucial in maintaining operational safety. Group supervisors use tools that estimate upcoming traffic load to aid decision-making. However, industry-standard models can fail to capture the nuances of upcoming air traffic complexity. This study presents a probabilistic approach to forecast the complexity of an airspace sector using the number of relevant aircraft pairs, i.e., those that require monitoring or deconfliction by a controller, as a proxy measure for ATCO workload. We adapted an existing filter algorithm to make it suitable for use in London Middle Sector (LMS), a complex airspace sector with multiple flows of traffic above some of the busiest airports in Europe. Through iterative feedback with ATCOs, the algorithm was refined and extended to handle specific geometric and operational considerations. The updated algorithm outperformed the original, with an F1-score of 0.84 compared to 0.69 on a labelled set of 50 traffic scenarios. To produce forecasts of future numbers of relevant aircraft pairs in the sector, a graph representation of the LMS route network was constructed, standardising the spatial fidelity of route legs. The forecasting method accounts for uncertainty in aircraft arrival times by modelling the probability of each aircraft occupying route segments at future query times. When combined with historic distributions of relevant interactions and a live operational data stream, predictions of upcoming ATCO workload could be made up to 45 minutes in advance. The proposed method to forecast upcoming workload showed a significantly stronger correlation with actual relevant interactions (Spearman's $ρ= 0.68$) than a standard traffic volume prediction ($ρ= 0.55$). The resulting data-driven tool shows promise for use by group supervisors to inform sector configuration and ATCO rostering decisions.

Related papers

• DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction [54.94692733670454]
  DESCENT is a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints.<n>Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context.<n>Our evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-08-26T16:49:50Z)
• ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management [60.85136195443409]
  We propose ASPaeroFlow: a for the joint Air Traffic Flow and Capacity Management (ATFCM)<n>It combines instance-space decompositions with a local exact approach using Answer Set Programming.<n>We benchmark ASPaeroFlow from small to industry-sized instances and compare it with exact and alternative approaches.
  arXiv  Detail & Related papers  (2026-08-10T08:57:18Z)
• Joint Air Traffic Flow and Capacity Management via Answer Set Programming [64.47906587579985]
  We introduce a joint ATFCM model with an encoding in Answer Set Programming (ASP)<n>Our results indicate that the ASP model outperforms the MIP model.<n>While DAC has the largest improvement on solving performance compared to rerouting, unrestricted variants of DAC or rerouting lead to search space thrashing.
  arXiv  Detail & Related papers  (2026-06-22T07:58:54Z)
• ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace [54.94692733670454]
  ASCENT is a lightweight transformer-based model for multi-modal 3D aircraft trajectory forecasting.<n>It integrates domain-aware 3D coordinate normalization and parameterized predictions.
  arXiv  Detail & Related papers  (2026-03-17T14:16:10Z)
• Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity [0.00448108533967507]
  Real-time assessment of near-term Air Traffic Controller (ATCO) task demand is a critical challenge in an increasingly crowded airspace.<n>This work introduces an interpretable Graph Neural Network (GNN) framework to address this gap.<n>We derive an interpretable, per-aircraft task demand score by systematically ablating aircraft and measuring the impact on the model's predictions.
  arXiv  Detail & Related papers  (2025-07-17T17:02:42Z)
• A Predictive Services Architecture for Efficient Airspace Operations [35.21388806827219]
  Accurate estimates of future airport capacity and airspace density are vital for better airspace management.<n>Data management and query processing remain complex due to the vast volume of high-rate air traffic data.<n>This paper presents a data processing and predictive services architecture that ingests large, uncorrelated, and noisy streaming data to forecast future airspace system states.
  arXiv  Detail & Related papers  (2025-03-21T19:57:38Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.<n>A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• Big data-driven prediction of airspace congestion [40.02298833349518]
  We present a novel data management and prediction system that accurately predicts aircraft counts for a particular airspace sector within the National Airspace System (NAS) In the preprocessing step, the system processes the incoming raw data, reduces it to a compact size, and stores it in a compact database. In the prediction step, the system learns from historical trajectories and uses their segments to collect key features such as sector boundary crossings, weather parameters, and other air traffic data.
  arXiv  Detail & Related papers  (2023-10-13T09:57:22Z)
• Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning [3.622365857213782]
  We propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities.
  arXiv  Detail & Related papers  (2023-07-20T03:54:47Z)
• Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs [47.18837782862979]
  This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. We demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues.
  arXiv  Detail & Related papers  (2023-03-10T02:22:33Z)
• Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM) [101.80862265018033]
  Urban air mobility (UAM) has been proposed in which vertical takeoff and landing (VTOL) aircraft are used to provide a ride-hailing service. In UAM, aircraft can operate in designated air spaces known as corridors, that link the aerodromes. A reliable communication network between GBSs and aircraft enables UAM to adequately utilize the airspace.
  arXiv  Detail & Related papers  (2021-12-26T14:41:52Z)
• A Simplified Framework for Air Route Clustering Based on ADS-B Data [0.0]
  This paper presents a framework that can support to detect the typical air routes between airports based on ADS-B data. As a matter of fact, our framework can be taken into account to reduce practically the computational cost for air flow optimization.
  arXiv  Detail & Related papers  (2021-07-07T08:55:31Z)
• Prediction of Traffic Flow via Connected Vehicles [77.11902188162458]
  We propose a Short-term Traffic flow Prediction framework so that transportation authorities take early actions to control flow and prevent congestion. We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by Connected Vehicles (CV) technology. We show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of various events that CV realistically encountered on segments along their trajectory.
  arXiv  Detail & Related papers  (2020-07-10T16:00:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.