FollowerStopper String Stability

Simulation framework for evaluating string stability in mixed-autonomy traffic using the FollowerStopper controller.
This project integrates SUMO (Simulation of Urban MObility) with Python to analyze wave-dampening performance, headway stability, and autonomous vehicle behaviors across different road network types.

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Features

• Supports multiple network types: Ring, Straight, and Circular.
• Mixed-autonomy simulation: Human-driven and autonomous vehicles in one environment.
• String stability metrics: Headway, velocity amplification, spacing error analysis.
• Customizable parameters: Reference speed, simulation duration, network topology.
• Visualization tools: Automated plots of vehicle speeds and spacing over time.
• Export utilities: Saves simulation data in .csv and figures in .pdf format.

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Project Structure

FollowerStopper-String-Stability/
│
├── main.py                 # Main entry point for running simulations
├── controller/             # Route generation, vehicle config, simulation logic
├── analysis/               # Plotting scripts and data analysis notebooks
├── figures/                # Auto-generated plots
├── output/                 # Simulation results (CSV files)
├── sumo_config/            # SUMO network and route configuration files
└── requirements.txt        # Python dependencies (to be created)

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Installation

1. Clone the Repository

git clone https://github.com/nsriad/FollowerStopper-String-Stability.git
cd FollowerStopper-String-Stability

2. Set up Python Environment

We recommend using Python 3.8+ and virtualenv:

python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Install SUMO

• Download SUMO and add it to your system PATH.
• Ensure sumo and sumo-gui commands work in your terminal.

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Usage

Run a simulation from the project root:

python main.py

Key parameters in main.py:

• network_type: ring, straight, or circular
• ref_speed: Desired reference speed (m/s)
• interrupt_time: Simulation duration (seconds)
• freq: Sampling frequency (Hz)

Simulation generates:

• Plots → figures/<network_type>/
• CSV Data → output/<network_type>/

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Example Output

After running:

python main.py

You will find:

• Vehicle speed plots: figures/<network_type>/...pdf
• Simulation data: output/<network_type>/...csv

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Dependencies

To be listed in requirements.txt:

traci
sumolib
matplotlib
numpy
pandas

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Citation

Will be added later after publication.

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## License
This project is released under the MIT License.