# Model and Data: To signal or not to signal? A non-cooperative game-theoretic approach to discretionary communication between road users

## About the repository

### Title

Model and Data: To signal or not to signal? A non-cooperative game-theoretic approach to discretionary communication between road users

### Creators

- **Isam Bitar, Msc**: concept, development, testing, execution, primary authorship
- **Albert Solernou Crusat, PhD**: concept, technical advice, review, supervisory team
- **Richard Romano, PhD**: review, supervisory team
- **David Watling, PhD**: concept, theory advice, review, primary supervisor

### Organisation

Institute for Transport Studies, University of Leeds

### Rights-holder

Copyright 2024 University of Leeds

### Publication year

2024

### Description

This project contains the Python files which constitute the simulation package used in the paper titled "To signal or not to signal? A non-cooperative game-theoretic approach to discretionary communication between road users" by Isam Bitar, Albert Crusat, Richard Romano and David Watling. The model is simulates a discretionary merge scenario based on bi-directional General Motors Car Following Model. A full description of the model can be found in the associated paper.

The project was developed and run in Python 3.11.0 64-bit.

### Cite as

Isam Bitar, Albert Solernou Crusat, Richard Romano, David Watling (2024): Model and Data: To signal or not to signal? A non-cooperative game-theoretic approach to discretionary communication between road users. [Dataset]. https://doi.org/10.5518/1608

### Related publication

Isam Bitar, Albert Solernou Crusat, Richard Romano, David Watling (2024): To signal or not to signal? A non-cooperative game-theoretic approach to discretionary communication between road users. Springer Nature Computer Science. Submitted.

## Terms of use

This project is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/.

## Project funding and information

This work was supported by the Engineering and Physical Sciences Research Council Doctoral Training Partnership, Grant No. EP/R513258/1.

## Run instructions

To run the simulation model as reported in the paper, simply open `Main.py` in a compatible development environment (Visual Studio Code recommended) and run. The default parameters will run the _Blind_ ruleset (codename `'Mirror'`). To run the _Transparent_ ruleset, simply change Line 66 to `intelligence = 'Transparent'`.

You must have the [Python 3.11.0](https://www.python.org/) or later installed on your machine to run the code, in addition to the following Python libraries:

- [NumPy](https://numpy.org/)

## Paper data

The data used in the paper are stored in the included `Results.zip`.

## Contact

For any queries, please contact Isam Bitar at [ts14isb@leeds.ac.uk](mailto:ts14isb@leeds.ac.uk).