A Genetic Algorithm-based BP Neural Network Method for Operational Performance Assessment of ATC Sector

  • Jianping Zhang The Second Research Institute of Civil Aviation Administration of China
  • Liwei Duan The Second Research Institute of Civil Aviation Administration of China
  • Jing Guo Civil Aviation Administration of China
  • Weidong Liu The Second Research Institute of Civil Aviation Administration of China
  • Xiaojia Yang The Second Research Institute of Civil Aviation Administration of China
  • Ruiping Zhang Southwest Regional Air Traffic Management Bureau of Civil Aviation of China
Keywords: air traffic control sector, operational performance, multivariate detection index system, genetic algorithm, back propagation neural network, comprehensive evaluation,

Abstract

To assess operational performance of air traffic control sector, a multivariate detection index system consisting of 5 variables and 17 indicators is presented, which includes operational trafficability, operational complexity, operational safety, operational efficiency, and air traffic controller workload. An improved comprehensive evaluation method, is designed for the assessment by optimizing initial weights and thresholds of back propagation (BP) neural network using genetic algorithm. By empirical study conducted in one air traffic control sector, 400 sets of sample data are selected and divided into 350 sets for network training and 50 sets for network testing, and the architecture of genetic algorithm-based back propagation (GABP) neural network is established as a three-layer network with 17 nodes in input layer, 5 nodes in hidden layers, and 1 node in output layer. Further testing with both GABP and traditional BP neural network reveals that GABP neural network performs better
than BP neural work in terms of mean error, mean square error and error probability, indicating that GABP neural network can assess operational performance of air traffic control sector with high accuracy and stable generalization ability. The multivariate detection index system and GABP neural network method in this paper can provide comprehensive, accurate, reliable and practical operational performance assessment of air traffic control sector, which enable the frontline of air traffic service provider to detect and evaluate operational performance of air traffic control sector in real time, and trigger an alarm when necessary.

Author Biographies

Jianping Zhang, The Second Research Institute of Civil Aviation Administration of China
ATM Engineering Technology Institute of CAAC
Liwei Duan, The Second Research Institute of Civil Aviation Administration of China
ATM Engineering Technology Institute of CAAC
Jing Guo, Civil Aviation Administration of China
ATM
Weidong Liu, The Second Research Institute of Civil Aviation Administration of China
Director
Xiaojia Yang, The Second Research Institute of Civil Aviation Administration of China
ATM Engineering Technology Institute of CAAC
Ruiping Zhang, Southwest Regional Air Traffic Management Bureau of Civil Aviation of China
ATM

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Published
2016-12-12
How to Cite
1.
Zhang J, Duan L, Guo J, Liu W, Yang X, Zhang R. A Genetic Algorithm-based BP Neural Network Method for Operational Performance Assessment of ATC Sector. PROMET [Internet]. 2016Dec.12 [cited 2020Sep.21];28(6):563-74. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/2003
Section
Articles