DOI: 10.4121/54825f14-8743-447d-8346-3afa46d319d6
title: Supporting data and code for Decentralised Traffic Management for Constrained Urban Airspace: Dynamically Generating and Acting Upon Aggregate Flow Data

description: 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 parametersIn depth results of example scenarios.BlueSky simulator code to reproduce the simulations.Logs of the simulation scenarios.Post processing code for the scenarios to generate the plots in the paper.Animations 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. We also performed some additional simulations to test the method in a different virtual network (Vienna).<br><strong>2. BlueSky Simulator code</strong>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. 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. These are found in the main_experiment_logs.zip<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. The code can be found in main_experiment_post_processing.zip and the box plot can be found in main_experiment_box_plots.zip. For the sensitivity analysis and example scenarios this is found in the sensitivity_analysis_plots.zip and in the example_scenario_post_processing.zip files, respectively.<br><strong>5. Animations 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. See the file named position_heat_map_animations.zip. Additionally, there is a video demo of the method found in https://www.youtube.com/watch?v=O8tEs_YWH1w<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.