A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering

12 pages•Published: December 23, 2019

Abstract

Problems tackled by researchers and data scientists in aviation and air traffic management (ATM) require manipulating large amounts of data representing trajectories, flight parameters and geographical descriptions of the airspace they fly through. The traffic library for the Python programming language defines an interface to usual processing and data analysis methods to be applied on aircraft trajectories and airspaces. This paper presents how traffic accesses different sources of data, leverages processing methods to clean, filter, clip or resample trajectories, and compares trajectory clustering methods on a sample dataset of trajectories above Switzerland.

Keyphrases: ads b, air traffic management, clustering, data processing, machine learning, mode~s, trajectory

In: Christina Pöpper and Martin Strohmeier (editors). Proceedings of the 7th OpenSky Workshop 2019, vol 67, pages 73-84.

Links:	https://easychair.org/publications/paper/BXjT
	https://doi.org/10.29007/sf1f

BibTeX entry

@inproceedings{OpenSky19:Python_Toolbox_Processing_Air,
  author    = {Xavier Olive and Luis Basora},
  title     = {A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering},
  booktitle = {Proceedings of the 7th OpenSky Workshop 2019},
  editor    = {Christina Pöpper and Martin Strohmeier},
  series    = {EPiC Series in Computing},
  volume    = {67},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/BXjT},
  doi       = {10.29007/sf1f},
  pages     = {73-84},
  year      = {2019}}

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