Abstract
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.
Highlights
Airspace is the carrier of air traffic system, and air traffic controllers (ATCos) are responsible for its safe and efficient operation
On the radar chart of the training set (see Figure 6(a)), SOCNN, random forest (RF), AdaBoost, and multilayer perception (MLP) all have achieved excellent results. e evaluation metrics such as Acc, recall, precision, and F1 score have reached more than 80%, and the accuracy of RF and SOCNN in the training set is almost close to 100%, which reflects that these two algorithms have strong learning capabilities for existing samples
We found that the data augmentation of random rotation will improve the performance of sector operation complexity (SOC) evaluation, but the setting of the random rotation angle range will have different effects on the final result
Summary
Airspace is the carrier of air traffic system, and air traffic controllers (ATCos) are responsible for its safe and efficient operation. In order to regulate air traffic safely, airspace is divided into several smaller sectors which are in charge of ATCos. As the air transport industry is developing rapidly, the surging flight volume and limited airspace have imposed a higher workload on ATCos. According to researches, the high workload of ATCos is more likely to lead to operational errors [1]. Intending to properly divide airspace sectors and efficiently manage air traffic flow so that the traffic control workload of ATCos can be kept below the maximum limit, it is necessary to determine an authoritative indicator that can reflect sector control workload accurately and objectively [2]. SOC is more specific because it specifies the “sector” area rather than a point, an airway, or other airspace elements, and it can distinguish studies on traffic pattern complexity from our “operational” complexity study [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
