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

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. *e operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flowmanagement, and allocation of air traffic controller resources. *erefore, 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 CNNmodel is introduced to automatically extract highlevel traffic features fromMTSIs and learn the SOC pattern. *us, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario.*emodel output is SOC levels for the target sector.*e experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information fromMTSIs and achieve promising performance than traditionalmachine learningmethods. In practice, ourwork can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors.