Sector Complexity Measures: A Comparison

Rahman, Siti Mariam Abdul; Borst, Clark; Mulder, Max

doi:10.11113/jt.v76.5923

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The SSD was originally designed as a decision-support tool that integrates various critical parameters of the Conflict Detection and Resolution problem [33][34][35]. This was later extended to complexity and workload analysis [11,[36][37][38], automated conflict resolution [15,16,39] and even ATC training [40]. Based on the findings of these studies, this research hypothesizes that an SSD image as learning feature contains sufficient information concerning air traffic conflicts to make an informed decision.…”

Section: B Solution Space Diagrammentioning

confidence: 99%

Toward Individual-Sensitive Automation for Air Traffic Control Using Convolutional Neural Networks

Rooijen

Ellerbroek

Borst

et al. 2020

Journal of Air Transportation

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Lack of trust and acceptance caused by strategic mismatches in problem-solving have been identified as obstacles in the introduction of workload-alleviating automation in air traffic control. One possible way to overcome these obstacles is by creating automation capable of providing personalized advisories conformal to the individual controller. This paper focuses on performing an exploratory investigation into the tools and methodology required for creating conformal automation. Central in the creation of individualized prediction models is the combination of a visual feature, capturing traffic situations, and a tailored convolutional neural network model trained on individual controller data recorded from a human-in-the-loop simulation. The main advantage of using a visual feature is that it could facilitate 'transparency' of the machine learning model. Results show that the trained models can reasonably predict command type, direction and magnitude. Furthermore, a correlation is found between controller consistency and achieved prediction performance. A comparison between individual-sensitive and general models showed a benefit of individually trained models, confirming the strategy heterogeneity of the population, which is a critical assumption for personalized automation. Future research should be done in refining the model architecture, finding richer visual features that capture the breadth of human decision-making behavior and feedback model outputs back to individuals for measuring controller agreement.

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Section: B Solution Space Diagrammentioning

confidence: 99%

Toward Individual-Sensitive Automation for Air Traffic Control Using Convolutional Neural Networks

Rooijen

Ellerbroek

Borst

et al. 2020

Journal of Air Transportation

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“…Although air traffic complexity and workload are not completely equivalent, it is reasonable to evaluate the workload by air traffic complexity. e reason lies in the subjective factor is so uncertain and complex that it is necessary for us to quantitatively evaluate the workload in an ATCo-independent way [8]. Note that we refer to the concept of "sector operation complexity (SOC)" from [2] to represent the air traffic complexity of a sector.…”

Section: Introductionmentioning

confidence: 99%

Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation

Xie

Zhang

et al. 2021

Complexity

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A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity (SOC) is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image (MTSI), is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors.

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