Supporting data and code for Decentralised Traffic Management for Constrained Urban Airspace: Dynamically Generating and Acting Upon Aggregate Flow Data

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.

本仓库为期刊论文《城市空域分散式分离：动态生成聚合流数据并基于其开展决策》（Decentralised separation for urban airspace: dynamically generating and acting upon aggregate flow data）提供配套支撑数据与代码。
本仓库的核心组成部分如下：聚类参数敏感性分析、示例场景深度仿真结果、用于复现仿真的BlueSky模拟器代码、仿真场景日志、用于生成论文中配图的场景后处理代码、展示全城空域场景运行情况的动画文件，以及Python环境配置说明。

1. 敏感性分析
本部分包含论文中提及的敏感性分析内容，用于测试聚类距离、高密度空域占比以及额外成本乘数，以筛选出适配本研究的最优参数组合。本部分包含敏感性分析PDF文档与绘图代码。此外，我们还在另一套虚拟空域网络（维也纳）中开展了额外仿真以验证所提方法的有效性。

2. BlueSky模拟器代码
本部分包含用于仿真本研究场景的BlueSky模拟器代码，对应文件为bluesky.zip。需注意，本次提供的代码是基于https://github.com/amorfinv/bluesky/tree/rotterdam 仓库代码的精简版本。模拟器代码中同时附带了所需插件与仿真场景，其中插件基于https://github.com/amorfinv/bluesky_plugins 仓库中的插件开发。请参阅本仓库提供的HOWTOSCENARIOS.md文件以了解场景运行方法，同时请确保安装兼容的Python环境。

3. 仿真日志
本部分包含论文中使用的仿真运行结果，不包含敏感性分析相关的仿真日志，仅涵盖期刊论文正文中使用的仿真数据。可通过HOWTOSCENARIOS.md文件中说明的方法运行仿真以复现这些结果，相关文件存放于main_experiment_logs.zip中。

4. 后处理代码与额外配图
本部分包含用于生成论文配图的后处理代码，同时附带了部分未在论文中展示的额外配图。请参阅HOWTOCREATEPLOTS.md文件以了解配图复现方法。代码文件存放于main_experiment_post_processing.zip中，箱线图相关代码存放于main_experiment_box_plots.zip中；敏感性分析与示例场景的后处理代码则分别存放于sensitivity_analysis_plots.zip与example_scenario_post_processing.zip文件中。

5. 全城空域场景动画
本部分包含若干全城空域场景的GIF动画，用于展示包含400架航空器的单一场景中交通流随时间的变化情况，相关文件存放于position_heat_map_animations.zip中。此外，本研究的方法演示视频可访问https://www.youtube.com/watch?v=O8tEs_YWH1w 查看。

6. Python环境配置说明
本部分包含用于开展全场景仿真、后处理以及配图生成的Python环境配置说明。本研究使用Conda环境进行开发，所需依赖包除BlueSky本身的依赖外，还包括geopandas、osmnx与seaborn。