303 research outputs found

    Forecasting workload and airspace configuration with neural networks and tree search methods

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    International audienceThe aim of the research presented in this paper is to forecast air traffic controller workload and required airspace configuration changes with enough lead time and with a good degree of realism. For this purpose, tree search methods were combined with a neural network. The neural network takes relevant air traffic complexity metrics as input and provides a workload indication (high, normal, or low) for any given air traffic control (ATC) sector. It was trained on historical data, i.e. archived sector operations, considering that ATC sectors made up of several airspace modules are usually split into several smaller sectors when the workload is excessive, or merged with other sectors when the workload is low. The input metrics are computed from the sector geometry and from simulated or real aircraft trajectories. The tree search methods explore all possible combinations of elementary airspace modules in order to build an optimal airspace partition where the workload is balanced as well as possible across the ATC sectors. The results are compared both to the real airspace configurations and to the forecast made by flow management operators in a French "en-route" air traffic control centre

    A participatory design for the visualization of airspace configuration forecasts

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    International audienceCurrently, airspace-related activities in Air Traffic Control Centers (ATCC) are dispatched between the Flow Management Position (FMP) operators and the control room manager, and take place in two different time frames. The first activity (FMP) is the planning, 2 days ahead, of airspace usage and anticipated overloads, using coarse-grain and relatively inaccurate workload prediction metrics. The second activity (control room manager) is the day-to-day operation, where workload is re-assessed in real-time and where airspace may be re-configured according to the actual traffic of the day. In previous works, a workload model relying on relevant air traffic complexity metrics was proposed, using a neural network trained on past sector operations. This workload prediction model was combined with tree search algorithms, in order to compute optimal partitions of the airspace in Air Traffic Control (ATC) sectors. This method provides more accurate airspace configuration forecasts than today, thus improving the overall predictability of the Air Traffic Management (ATM)/ATC system. When relying on accurate 4D-trajectory predictions, as expected in the SESAR program, it could contribute towards bridging the current gap between the pre-tactical airspace/flow management and real-time operations. In this paper, we detail the participatory design approach that we used to develop a research prototype displaying the algorithm's results. As there is no such forecasting tool today, the main issue was to create a user interface in the absence of an existing user

    A Spatiotemporal Hybrid Model for Airspace Complexity Prediction

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    Configuring Airspace Sectors with Approximate Dynamic Programming

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    In response to changing traffic and staffing conditions, supervisors dynamically configure airspace sectors by assigning them to control positions. A finite horizon airspace sector configuration problem models this supervisor decision. The problem is to select an airspace configuration at each time step while considering a workload cost, a reconfiguration cost, and a constraint on the number of control positions at each time step. Three algorithms for this problem are proposed and evaluated: a myopic heuristic, an exact dynamic programming algorithm, and a rollouts approximate dynamic programming algorithm. On problem instances from current operations with only dozens of possible configurations, an exact dynamic programming solution gives the optimal cost value. The rollouts algorithm achieves costs within 2% of optimal for these instances, on average. For larger problem instances that are representative of future operations and have thousands of possible configurations, excessive computation time prohibits the use of exact dynamic programming. On such problem instances, the rollouts algorithm reduces the cost achieved by the heuristic by more than 15% on average with an acceptable computation time

    Determining Air Traffic Complexity – Challenges and Future Development

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    Air traffic complexity is one of the main drivers of the air traffic controllers’ workload. With the forecasted increase of air traffic, the impact of complexity on the controllers\u27 workload will be even more pronounced in the coming years. The existing models and methods for determining air traffic complexity have drawbacks and issues which are still an unsolved challenge. In this paper, an overview is given of the most relevant literature on air traffic complexity and improvements that can be done in this field. The existing issues have been tackled and new solutions have been given on how to improve the determination of air traffic complexity. A preliminary communication is given on the future development of a novel method for determining air traffic complexity with the aim of designing a new air traffic complexity model based on air traffic controller tasks. The novel method uses new solutions, such as air traffic controller tasks defined on pre-conflict resolution parameters, experiment design, static images of traffic situations and generic airspace to improve the existing air traffic complexity models.</p

    Air Traffic Complexity as a Source of Risk in ATM

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    In this chapter the connection between air traffic complexity and risks in air traffic management system will be explored. Air traffic complexity is often defined as difficulty of controlling a traffic situation, and it is therefore one of the drivers for air traffic controller’s workload. With more workload, the probability of air traffic controller committing an error increases, so it is necessary to be able to assess and manage air traffic complexity. Here, we will give a brief overview of air traffic complexity assessment methods, and we will put the traffic complexity assessment problem into a broader context of decision complexity. Human reliability assessment methods relevant to air traffic management will be presented and used to assess the risk of loss of separation in traffic situations with different levels of complexity. To determine the validity of the human reliability assessment method, an analysis of conflict risk will be made based on the real-time human-in-the-loop (HITL) simulations

    A Deep Unsupervised Learning Approach for Airspace Complexity Evaluation

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    Airspace complexity is a critical metric in current Air Traffic Management systems for indicating the security degree of airspace operations. Airspace complexity can be affected by many coupling factors in a complicated and nonlinear way, making it extremely difficult to be evaluated. In recent years, machine learning has been proved as a promising approach and achieved significant results in evaluating airspace complexity. However, existing machine learning based approaches require a large number of airspace operational data labeled by experts. Due to the high cost in labeling the operational data and the dynamical nature of the airspace operating environment, such data are often limited and may not be suitable for the changing airspace situation. In light of these, we propose a novel unsupervised learning approach for airspace complexity evaluation based on a deep neural network trained by unlabeled samples. We introduce a new loss function to better address the characteristics pertaining to airspace complexity data, including dimension coupling, category imbalance, and overlapped boundaries. Due to these characteristics, the generalization ability of existing unsupervised models is adversely impacted. The proposed approach is validated through extensive experiments based on the real-world data of six sectors in Southwestern China airspace. Experimental results show that our deep unsupervised model outperforms the state-of-the-art methods in terms of airspace complexity evaluation accuracy

    RNN-CNN hybrid model to predict C-ATC CAPACITY regulations for en-route traffic

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    Meeting the demand with the available airspace capacity is one of the most challenging problems faced by Air Traffic Management. Nowadays, this collaborative Demand–Capacity Balancing process often ends up enforcing Air Traffic Flow Management regulations when capacity cannot be adjusted. This process to decide if a regulation is needed is time consuming and relies heavily on human knowledge. This article studies three different Air Traffic Management frameworks aiming to improve the cost-efficiency for Flow Manager Positions and Network Manager operators when facing the detection of regulations. For this purpose, two already tested Deep Learning models are combined, creating different hybrid models. A Recurrent Neural Network is used to process scalar variables to extract the overall airspace characteristics, and a Convolutional Neural Network is used to process artificial images exhibiting the specific airspace configuration. The models are validated using historical data from two of the most regulated European regions, resulting in a novel framework that could be used across Air Traffic Control centers. For the best hybrid model, using a cascade architecture, an average accuracy of 88.45% is obtained, with an average recall of 92.16%, and an average precision of 86.85%, across different traffic volumes. Moreover, two different techniques for model explainability are used to provide a theoretical understanding of its behavior and understand the reasons behind the predictionsThis work was funded EUROCONTROL under Ph.D. Research Contract No. 18-220569-C2 and by the Ministry of Economy, Industry, and Competitiveness of Spain under GrantNumber PID2020-116377RB-C21. This project has also received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 783287.Peer ReviewedPostprint (published version

    Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning

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    Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{Link\mathsf{Link}}
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