DOI QR코드

DOI QR Code

A Take-off Clearance Prediction Model for Mixed Mode Runway Operations

출·도착 혼합 사용 활주로에서의 관제사 이륙 허가 예측 모형 개발

  • 홍성권 (한국항공우주연구원 위성항법.응용기술센터) ;
  • 전대근 (한국항공우주연구원 위성항법.응용기술센터) ;
  • 김현경 (한국항공우주연구원 위성항법.응용기술센터)
  • Received : 2016.08.21
  • Accepted : 2016.09.29
  • Published : 2016.09.30

Abstract

This paper proposes a prediction model of air traffic controller's take-off clearance under mixed mode runway operations. The proposed model has its purpose on the better prediction of the air traffic controller's clearance on take-offs of departure aircraft by considering various factors. For this purpose, support vector machine classification algorithm is used for the proposed model. The proposed model is applied to real air traffic operations to demonstrate its performances.

Acknowledgement

Grant : 항공기 출발 및 도착 통합 관리 기술 연구

Supported by : 국토교통부

References

  1. Feron, E., Hansman, R. J., Odoni, A. R., Cots, R. B., Delcaire, B., Feng, X., Hall, W. D., Idris, H. R., Muharremoglu, A., and Pujet, N., "The Departure Planner: A Conceptual Discussion," International Center for Air Transportation reports, 1997.
  2. Doble, N. A., Timmerman, J., Carniol, T., Klopfenstein, M., Tanino, M., and Sud, V., "Linking Traffic Management to the Airport Surface: Departure Flow Management to the Airport Surface: Departure Flow Management and Beyond," 8th USA/Europe Air Traffic Management R&D Seminar, CA, 2009.
  3. Waqar, M., Gupta, G., and Jung, Y. C., "Managing departure aircraft release for efficient airport surface operations," AIAA Guidance, Navigation, and Control Conference, Toronto, 2010.
  4. FAA, Air Traffic Control, Order JO 7110.65W, 2015.
  5. 국토교통부, 항공교통관제절차, 국토교통부 고시 제2015-410호, 2015.
  6. Burges, C. J. C., "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 121-167. https://doi.org/10.1023/A:1009715923555
  7. Liu, H. X., Zhang, R. S., Luan, F., Yao X. J., Liu, M. C., Hu, Z. D., and Fan, B. T., "Diagnosing Breast Cancer Based on Support Vector Machines," Journal of Chemical Information and Modeling, Vol. 43, No. 3, 2003, pp. 900-907.
  8. Joachims, T., "Text Categorization with Support Vector Machines: Learning with Many Relevant Features," '98 Proceedings of the 10th European Conference on Machine Learning, 1998.
  9. Hosmer, D. W. and Lemeshow, S., Applied Logistic Regression, John Wiley & Sons, Inc, New York, 2000.
  10. Xu, Q. S., and Liang, Y. Z., "Monte Carlo Cross Validation," Chemometrics and Intelligent Laboratory Systems, Vol. 56, No. 1, 2001, pp. 1-11. https://doi.org/10.1016/S0169-7439(00)00122-2
  11. Raschka, S., "An Overview of General Performance Metrics of Binary Classifier Systems," arXiv preprint arXiv:1410.5330, 2014.
  12. Matthews, B. W., "Comparison of the Predicted and Observed Secondary Structure of T4 Phage Lysozyme," Biochimica et Biophysica Acta (BBA)-Protein Structure, 405:442-451, 1975. https://doi.org/10.1016/0005-2795(75)90109-9