Metadata-Version: 2.4
Name: pyCARM
Version: 1.0.3
Summary: pyCARM: Cellular Automata for Aircraft Arrival Modelling
Home-page: https://github.com/IkeoluwaSta/pyCARM
Author: The Hong Kong University of Science
Author-email: iiogedengbe@connect.ust.hk
Project-URL: Source Code, https://github.com/IkeoluwaSta/pyCARM
Project-URL: Bug Tracker, https://github.com/IkeoluwaSta/pyCARM/issues
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: distinctipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python
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# Project

## pyCARM - Cellular Automata for Aircraft Arrival Modeling

This repository contains some of the source code used for the paper titled *Cellular automata for the investigation of navigation dynamics and aircraft mix in terminal arrival traffic*. Physica A, 671 (2025), 130628. https://doi.org/10.1016/j.physa.2025.130628. The article is available online at [Link](https://doi.org/10.1016/j.physa.2025.130628) and in the [docs](docs) folder. 

# Introduction
Investigating the impact of traffic mix and route flexibility on the arrival traffic dynamic within the terminal maneuvering area (TMA) is challenging, mainly due to the spatial constraints and wake turbulent separation requirements. In this study, we capture the dynamism of complex interactions and non-linearity in traffic by using a cellular automaton that is modified to enable more realistic representation of air traffic movements. Our results show that route flexibility makes traffic less sensitive to changes caused by size-based traffic mix and demonstrate the emergence of an organized flow zone in the fundamental diagram of the flexible strategies. When a gentle TMA saturation behavior is preferred, however, less flexible routes are deemed more suitable. As a general principle, we propose to adopt a mixed strategy that uses a fixed
routing strategy at low TMA occupancies and a flexible routing strategy at medium to high TMA occupancies.

<img width="607" height="370" alt="Image" src="https://github.com/user-attachments/assets/f73621d4-d229-4439-82db-e0015056b3c7" />

# Dependencies
numpy

distinctipy

matplotlib

Collections


# Citation

```
@article{ogedengbe2025cellular,
  title={Cellular automata for the investigation of navigation dynamics and aircraft mix in terminal arrival traffic},
  author={Ogedengbe, Ikeoluwa Ireoluwa and Tai, Tak Shing and Wong, KY Michael and Liem, Rhea P},
  journal={Physica A: Statistical Mechanics and its Applications},
  pages={130628},
  year={2025},
  publisher={Elsevier}
}

```

# References
[1] Ogedengbe, I. I., Tai, T. S., Wong, K. M., & Liem, R. P. (2025). Cellular automata for the investigation of navigation dynamics and aircraft mix in terminal arrival traffic. Physica A: Statistical Mechanics and its Applications, 130628.

[2] Ogedengbe, I. I., Wong, M. K., & Liem, R. P. (2023). A Comparative Analysis of Terminal Area Navigation and Conventional Standard Arrival Routes with Cellular Automata. In AIAA AVIATION 2023 Forum (p. 3969).

