Clustering aircraft trajectories with recursive DBSCAN
The clustering algorithm trdbscan is based on the recursive DBSCAN method introduced in the following scientific paper:
C. E. Verdonk Gallego, V. F. Fernando Gómez, F.J. Saez Nieto, and M.G. Martinez, "Discussion On Density-Based Clustering Methods Applied for Automated Identification of Airspace Flows," in 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC). IEEE, 2018. p. 1-10.
As this method proves useful in the context of the identification of traffic flows (clusters of aircraft trajectories), the trdbscan algorithm is used with the Swiss traffic example provided by the traffic library. Results are plotted with the sectflow library and can be compared with the DBSCAN method provided as an example in the traffic documentation, or the TrajClust method (also based on a recursive DBSCAN) supplied by the sectflow library and described in the following scientific paper:
L. Basora, V. Courchelle, J. Bedouet and T. Dubot, "Occupancy Peak Estimation from Sector Geometry and Traffic Flow," in Proceedings of the SESAR Innovation Days, 2018
The main advantages of the trdbscan method are:
- no need to specify the epsilon parameter of the DBSCAN algorithm, automatically computed at each iteration by the KNN elbow method
- anomalies are stored (anomalies_) and classified: largest anomalies met at iteration 1 with a large epsilon are the first elements of the anomalies_ list
Example: main traffic flows from flight trajectories over Switzerland on August 1st 2018
Running the tests
If you want to run the demo_trdbscan notebook, you need to:
- install the traffic library
- install the sectflow library
- copy the trdbscan.py class in your working folder
Built With
On top of classical Python libraries (matplotlib, collections, numpy, logging, pandas), the following libraries are used:
- kneed - Knee point detection in Python
- traffic library - Air traffic data processing in Python
- scikit-learn - Machine Learning in Python
- sectflow library - Python library to identify air traffic flows
Authors
- Thomas Dubot
License
This project is licensed under the MIT License - see the LICENSE.md file for details