Result: A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering
Title:
A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering
Authors:
Contributors:
DTIS, ONERA, Université de Toulouse [Toulouse], ONERA-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)
Source:
7th OpenSky Workshop 2019. :73
Publisher Information:
CCSD, 2019.
Publication Year:
2019
Collection:
collection:ONERA
collection:ONERA-MIP
collection:DTIS_ONERA
collection:ONERA-MIP
collection:DTIS_ONERA
Subject Terms:
Subject Geographic:
Original Identifier:
HAL: hal-02650267
Document Type:
Conference
conferenceObject<br />Conference papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.29007/sf1f
DOI:
10.29007/sf1f
Access URL:
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.02650267v1
Database:
HAL
Further Information
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.