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. 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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6457246 |
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| author | Hua Xie Minghua Zhang Jiaming Ge Xinfang Dong Haiyan Chen |
| author_facet | Hua Xie Minghua Zhang Jiaming Ge Xinfang Dong Haiyan Chen |
| author_sort | Hua Xie |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7e5d22f7f18f4c8da00f28107da7c05c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7e5d22f7f18f4c8da00f28107da7c05c2025-02-03T06:06:30ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/64572466457246Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity EvaluationHua Xie0Minghua Zhang1Jiaming Ge2Xinfang Dong3Haiyan Chen4College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Computer Science and Technology/College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Computer Science and Technology/College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaA 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.http://dx.doi.org/10.1155/2021/6457246 |
| spellingShingle | Hua Xie Minghua Zhang Jiaming Ge Xinfang Dong Haiyan Chen Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation Complexity |
| title | Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation |
| title_full | Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation |
| title_fullStr | Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation |
| title_full_unstemmed | Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation |
| title_short | Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation |
| title_sort | learning air traffic as images a deep convolutional neural network for airspace operation complexity evaluation |
| url | http://dx.doi.org/10.1155/2021/6457246 |
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