A toolbox for processing and analysing air traffic data

The traffic library helps working with common sources of air traffic data.

Its main purpose is to offer basic cumbersome data analysis methods commonly applied to trajectories and ATC sectors. When a specific function is not provided, the access to the underlying structure is direct, through an attribute pointing to a pandas dataframe.

The library also offers facilities to parse and/or access traffic data from open sources of ADS-B traffic like the OpenSky Network or Eurocontrol DDR files. It is designed to be easily extendable to other sources of data.

Static visualisation (images) exports are accessible via Matplotlib/Cartopy. More dynamic visualisation frameworks are easily accessible in Jupyter environments with ipyleaflet and altair; or through exports to other formats, including CesiumJS or Google Earth.

Installation

Latest release

You may install traffic in a fresh conda environment:

# Recommended options if not set up yet
conda config --set channel_priority strict
conda config --add channels conda-forge

# Installation
conda create -n traffic -c conda-forge python=3.9 traffic

Adjust the Python version (>=3.7) and append packages you may need for future works (e.g. bpython, jupyterlab, etc.)

Then activate the environment each time you need to use the traffic library:

conda activate traffic

Warning:

Please only report installation issues in fresh conda environments.

Updating traffic

# -n option is followed by the name of the environment
conda update -n traffic -c conda-forge traffic

Development version

You may also install or update traffic in an existing environment with pip:

pip install --upgrade traffic

For the most recent development version, clone the Github repository:

git clone https://github.com/xoolive/traffic
cd traffic
pip install .[dev]

If you intend to file a pull request, please activate pre-commit hooks:

pre-commit install

For troubleshootings, refer to the appropriate documentation section.

Credits

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

@article{olive2019traffic,
    author={Xavier {Olive}},
    journal={Journal of Open Source Software},
    title={traffic, a toolbox for processing and analysing air traffic data},
    year={2019},
    volume={4},
    pages={1518},
    doi={10.21105/joss.01518},
    issn={2475-9066},
}

Additionally, you may consider adding a star to the repository. This token of appreciation is often interpreted as a positive feedback and improves the visibility of the library.

Documentation

Documentation available at https://traffic-viz.github.io/
Join the Gitter chat: https://gitter.im/xoolive/traffic

Tests and code quality

Unit and non-regression tests are written in the tests/ directory. You may run pytest from the root directory.

Tests are checked on Github Actions platform upon each commit. Latest status and coverage are displayed with standard badges hereabove.

In addition, code is checked against static typing with mypy (pre-commit hooks are available in the repository) and extra quality checks performed by Codacy.

Command line tool

The traffic tool scripts around the library for common usecases.

The most basic use case revolves around exploring the embedded data. You may check the help with traffic data -h.

traffic data -p Tokyo
     altitude country iata  icao   latitude   longitude                                name
3820       21   Japan  HND  RJTT  35.552250  139.779602  Tokyo Haneda International Airport
3821      135   Japan  NRT  RJAA  35.764721  140.386307  Tokyo Narita International Airport

Feedback and contribution

Any input, feedback, bug report or contribution is welcome.

Should you encounter any issue, you may want to file it in the issue section of this repository. Please first activate the DEBUG messages recorded using Python logging mechanism with the following snippet:

import logging
logging.basicConfig(level=logging.DEBUG)

Bug fixes and improvements in the library are also helpful.

If you share a fix together with the issue, I can include it in the code for you. But since you did the job, pull requests (PR) let you keep the authorship on your additions. For details on creating a PR see GitHub documentation Creating a pull request. You can add more details about your example in the PR such as motivation for the example or why you thought it would be a good addition. You will get feedback in the PR discussion if anything needs to be changed. To make changes continue to push commits made in your local example branch to origin and they will be automatically shown in the PR.

You may find the process troublesome but please keep in mind it is actually easier that way to keep track of corrections and to remember why things are the way they are.

Frequently asked questions

I want to know more about Eurocontrol NM files

We download these files from Eurocontrol Network Manager Demand Data Repository (DDR) under Dataset Files > Airspace Environment Datasets. Access conditions are managed by EUROCONTROL.

Should you have no such access, basic FIRs are provided in eurofirs from traffic.data.

I want to know more about Eurocontrol AIXM files

When you import aixm_airspaces from traffic.data, you need to set a path to a directory containing AIRAC files. These are XML files following the AIXM standard and produced by Eurocontrol. We download these files from Eurocontrol Network Manager B2B web services. You have to own a B2B certificate granted by EUROCONTROL to get access to this data.

What does AIRAC mean?

Aeronautical Information Publications are updated every 28 days according to fixed calendar. This cycle is known as AIRAC (Aeronautical Information Regulation And Control) cycle.

espinielli/traffic