Volume 41 Issue 2
Apr.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(2): 76-85
ZHU Chengyuan, ZHANG Che, GUAN Jianhua. A Method for Monitoring Traffic State in the Airspace Based on an Improved Support Vector Machine[J]. Journal of Transport Information and Safety, 2023, 41(2): 76-85. doi: 10.3963/j.jssn.1674-4861.2023.02.008
Citation: ZHU Chengyuan, ZHANG Che, GUAN Jianhua. A Method for Monitoring Traffic State in the Airspace Based on an Improved Support Vector Machine[J]. Journal of Transport Information and Safety, 2023, 41(2): 76-85. doi: 10.3963/j.jssn.1674-4861.2023.02.008
shu

A Method for Monitoring Traffic State in the Airspace Based on an Improved Support Vector Machine

doi: 10.3963/j.jssn.1674-4861.2023.02.008

  • College of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China

  • Received Date: 2022-03-07
    Available Online: 2023-06-19
    Abstract
  • This paper quantitatively analyzes the methods for monitoring airspace traffic state from the perspective of the workload of air traffic controllers, due to the difficult measurement of such a factor in current studies.In response to the need of monitoring airspace traffic state more efficiently, an airspace simulation model is developedbased onTotal Airspace and Airport Modeller (TAAM) software and a method for identifying traffic state in the rasterized airspace scenario is proposed based on an improved support vector machine (SVM). Based on real-world operation experience of controllers, the shape and size of grids are determined by comparing different rasterization schemes. Target airspace is rasterized by taking the hexagon with a side length of 25 km as the smallest unit. Considering a variety of controller's workloads and the distribution of navigation facilities, a set of indicators for describing traffic states of the airspace is developed.Ak-means clustering algorithm is used to generate prior classified data by aggregating simulated sample data. A traffic state model for the airspace, developed based on the sparrow-search algorithm (SSA) and SVM, iscalled SSA-SVM.The solution set is divided according to the fitness. Moreover, key parameters of the model, including kernel parameters σ and penalty coefficients C, are optimized to determine a combination of parameters, which can increase the generalization capability of the model and avoid overfitting. Traffic states in the rasterized airspace are divided into four levels. Simulations are conductedfor the control airspace ofthe City of Xi'an. Study results show that the proposed SSA-SVM model can mitigate the overfitting problem, but not by the proposed genetic algorithm and support vector machine (GA-SVM) model.The average accuracy of classification is improved by 2.50%, and the accuracy of classification is improved by 1.73%. Among the tested 176 grids, the number of congested, crowded, and steady grids are 26, 18, and 51, respectively. Compared with the partition method for the complex airspace based on controller experience, the convergencerate of the proposed model is as high as 95%, which verifies the effectiveness of the proposed method for identifying airspace traffic state and reducing the workload of air traffic controllers.

     

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