Exploration of Local Optimization Mode for Air Traffic Control Based on Deep Learning Algorithms

DOI

10.2991/978-94-6463-514-0_70How to use a DOI?

Keywords

Air traffic control system; Deep learning; Ant colony optimization algorithm

Abstract

In order to improve the efficiency and safety of air traffic control systems, a deep learning based optimization strategy is adopted, integrating data collection, processing, and decision support modules. By improving the ant colony optimization algorithm and neural network model, route scheduling and flight safety management are optimized. The results indicate that the system significantly improves decision-making accuracy and enhances the ability to respond to emergencies in various aviation control scenarios.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kechen Song
AU  - Nan Yang
PY  - 2024
DA  - 2024/09/28
TI  - Exploration of Local Optimization Mode for Air Traffic Control Based on Deep Learning Algorithms
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 720
EP  - 729
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_70
DO  - 10.2991/978-94-6463-514-0_70
ID  - Song2024
ER  -