Abstract
With the rapid increase in the number of flights all over the world, the management and control of flight operations has become difficult in recent years. Moreover, the expectations for the aviation sector indicate that this increase will continue in the upcoming years. Therefore, safer and systematic monitoring systems by eliminating the requirement of human-dependent tracking during the air travel of an aircraft and automating the detection of abnormal situations has become a major problem in aviation sector. With the recent advances in artificial intelligence, a safer and systematic tracking system for controlling the airspace by eliminating the need for human-dependent tracking during the flight of aircraft in the air has become possible. In this study, we aimed to create a system that detects and predicts movements to indicate abnormal, dangerous situations in the airspace by monitoring radar flight data using machine learning and deep learning techniques. We applied two different methods, i.e., Proximity Based kNN and Auto Encoder We used real-life historical radar flight data set which consists of Flight Radar 24 data were converted from ADS-B messages for learning. We created simulation data and used this data for testing and validation for our trained model. Within the scope of this project, we also developed a system to monitor air traffic through radar tracks with our model and present the abnormal situations to the user through a visual interface for decision support. In this visualization, we present the abnormal situations if one of the algorithms labeled as anomaly. Results for both methods have shown that our findings were similar to the real-life predictions.
Original language | English |
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Title of host publication | Computational Collective Intelligence - 13th International Conference, ICCCI 2021, Proceedings |
Editors | Ngoc Thanh Nguyen, Ngoc Thanh Nguyen, Lazaros Iliadis, Ilias Maglogiannis, Bogdan Trawiński |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 771-783 |
Number of pages | 13 |
ISBN (Print) | 9783030880804 |
DOIs | |
Publication status | Published - 2021 |
Event | 13th International Conference on Computational Collective Intelligence, ICCCI 2021 - Virtual, Online Duration: 29 Sept 2021 → 1 Oct 2021 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12876 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 13th International Conference on Computational Collective Intelligence, ICCCI 2021 |
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City | Virtual, Online |
Period | 29/09/21 → 1/10/21 |
Bibliographical note
Publisher Copyright:© 2021, Springer Nature Switzerland AG.
Keywords
- ADS-B
- Air traffic management
- Anomaly detection
- Auto encoder
- Deep learning
- Flight control
- Machine learning
- Proximity based kNN