Supporting code and data for: Dynamic capacity balancing in urban airspace: comparing historical and real-time aggregate flow data

doi:10.4121/c3a4912b-b248-4ad2-8c73-15b505f4287f.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/c3a4912b-b248-4ad2-8c73-15b505f4287f
Datacite citation style:
Morfin Veytia, Andres; Ellerbroek, Joost; Hoekstra, Jacco (2024): Supporting code and data for: Dynamic capacity balancing in urban airspace: comparing historical and real-time aggregate flow data. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/c3a4912b-b248-4ad2-8c73-15b505f4287f.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This repository contains some supporting data and code for the 2024 SESAR Innovation Days Conference paper "Dynamic capacity balancing in urban airspace: comparing historical and real-time aggregate flow data."


Note that this is a continuation of the work published here:


Morfin Veytia, Andres; Ellerbroek, Joost; Hoekstra, Jacco (2024): Supporting data and code for Decentralised Traffic Management for Constrained Urban Airspace: Dynamically Generating and Acting Upon Aggregate Flow Data. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/54825f14-8743-447d-8346-3afa46d319d6.v2


Therefore, much of the data and code is similar. However, this work provides some additional code and scenarios.


The main components need to reproduce the results are:


  1. BlueSky simulator code to reproduce the simulations.
  2. Post processing code for the scenarios to generate the plots in the paper and logs.
  3. Voronoi creation code
  4. Python environment description.


1. BlueSky Simulator code

This includes the BlueSky code for simulating the scenarios. This is the bluesky.zip folder. Note that the code provided is a condensed version of the one in https://github.com/amorfinv/bluesky/tree/rotterdam. The plugins and scenarios are also provided in the simulator code. The plugins are based on those based in the following repository, https://github.com/amorfinv/bluesky_plugins.


Refer to the README.md file provided to learn how to run the scenarios. Also, make sure to install a compatible python environment.


2. Post-processing code, plots, and logs

This includes the code to generate the plots seen in the paper and the logs of the simulations. It also includes some additional plots not shown in the paper. Read the README.md file for recreating the plots. This information can be found in main_experiment_results.zip. Some of the logs come from the previous work. The previous logs are labelled as real-time data labelling in this paper.


3. Voronoi creation code

The file called generate_voronois.zip includes the code to generate the voronoi code used for the historical data concept in the paper. Note that to generate the voronoi a more recent version of geopandas is necessary, so a different python environment is required. All you need is python=3.12, geopandas=1.0.1 and scikit-learn=1.5.1.


6. Python environment description

This includes the python environment used to simulate, post-process, and generate the plots. This work used conda environments. The main packages used are those required by BlueSky in addition to geopandas, osmnx, and seaborn. Note that the voronoi creation requires a different python environment.

history
  • 2024-10-30 first online, published, posted
publisher
4TU.ResearchData
format
zip files with *.py, *.png, *.csv, and *.gpkg
organizations
TU Delft, Faculty of Aerospace Engineering, Department of Control & Operations

DATA

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