International audience; 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.

Original document

The different versions of the original document can be found in:

Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.29007/sf1f
Licence: CC BY-NC-SA license

Document Score


Views 5
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?