Supporting data and code for:

This repository contains some supporting data and code for the journal paper Decentralised separation for urban airspace: dynamically generating and acting upon aggregate flow data<br>The main components are:Sensitivity analysis of clustering parametersBlueSky simulator code to reproduce the simulations.Logs of the simulation scenarios.Post processing code for the scenarios to generate the plots in the paper.GIFs showing animations of the city-wide scenariosPython environment description.<br><strong>1. Sensitivity analysis</strong>This includes the referenced sensitivity analysis in the paper. It tests the cluster distance, percent of high density airspace, and the additional cost multipliers to choose a well performing one for the paper. This includes the sensitivity analysis PDF file and the plotting code.<br><strong>2. BlueSky Simulator code</strong>This includes the BlueSky code for simulating the scenarios. Note that the code provided is a condensed version of the one in https://github.com/amorfinv/bluesky/tree/rotterdam. Note that 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.<br>Refer to the HOWTOSCENARIOS.md file provided to learn how to run the scenarios. Also, make sure you install a compatible python environment.<br><strong>3. Simulation logs</strong>This includes the result of the simulations ran in the paper. Note that it does not include those of the sensitivity analysis. It only includes those used in the journal paper. These can be reproduced by running the simulations as explained in the HOWTOSCENARIOS.md file.<br><strong>4. Post-processing code and other plots</strong>This includes the code to generate the plots seen in the paper. It also includes some additional plots not shown in the paper. Read the HOWTOCREATEPLOTS.md file for recreating the plots.<br><strong>5. GIFs of city-wide scenarios</strong>This includes some GIFs of the city-wide scenarios to show how the traffic looks like over time for one scenario with 400 aircraft.<br><strong>6. Python environment description</strong>This includes the python environment used to simulate, post-process, and generate the images for all scenarios. This work used conda environments. The main packages used are those required by BlueSky in addition to geopandas, osmnx, and seaborn.