If you find this project useful for your research and use it in an academic work, you may cite it as:

    author={Xavier {Olive}},
    journal={Journal of Open Source Software},
    title={traffic, a toolbox for processing and analysing air traffic data},

The following list contains publications from research using the traffic library:

  • S. Proud.
    Go-Around Detection Using Crowd-Sourced ADS-B Position Data.
    Aerospace, 2020, 7(2), 16
  • M. Schultz, X. Olive, J. Rosenow, H. Fricke, S. Alam.
    Analysis of airport ground operations based on ADS-B data. Proceedings of the 1st conference on Artificial Intelligence and Data Analytics in Air Transportation (AIDA-AT), 2020
  • M. Schultz, J. Rosenow and X. Olive.
    A-CDM Lite: situation awareness and decision making for small airports based on ADS-B data. Proceedings of the 9th SESAR Innovation Days, 2019.
  • X. Olive and L. Basora.
    Air Traffic Data Processing using Python: Trajectory Clustering.
    Proceedings of the 7th OpenSky Workshop, 2019.
  • M. Schäfer, X. Olive, M. Strohmeier, M. Smith, I. Martinovic, V. Lenders.
    OpenSky Report 2019: Analysing TCAS in the Real World using Big Data.
    Proceedings of the 38th Digital Avionics Systems Conference (DASC), 2019
  • X. Olive and L. Basora
    Identifying Anomalies in past en-route Trajectories with Clustering and Anomaly Detection Methods. Proceedings of the 13th Air Traffic Management R&D Seminar, 2019
  • X. Olive, J. Grignard, T. Dubot and J. Saint-Lot.
    Detecting Controllers’ Actions in Past Mode S Data by Autoencoder-Based Anomaly Detection. Proceedings of the 8th SESAR Innovation Days, 2018
  • X. Olive and P. Bieber.
    Quantitative Assessments of Runway Excursion Precursors using Mode S Data. Proceedings of the 8th International Conference on Research in Air Transportation, 2018 (Best paper award)
  • X. Olive and J. Morio.
    Trajectory clustering of air traffic flows around airports.
    Aerospace Science and Technology 84, 2019, pp. 776–781.