CA-ICA Traffic Optimization

Smart Traffic Optimization Using Cultural Algorithm & Imperialist Competitive Algorithm

📦 GitHub · 🚀 Installation · ✨ Features

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CA-ICA Traffic Optimization

CA-ICA Traffic Optimization is a smart transportation research project that applies two metaheuristic optimization algorithms — Cultural Algorithm (CA) and Imperialist Competitive Algorithm (ICA) — to solve intelligent urban traffic optimization problems.

The project focuses on improving traffic efficiency, optimizing transportation infrastructure, and balancing urban mobility systems through simulation and evolutionary computation techniques.

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About

CA-ICA Traffic Optimization is an intelligent traffic optimization system that uses evolutionary algorithms to simulate and optimize public transportation networks, bus allocation, and urban traffic center positioning for smart city environments.

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Tags

Traffic Optimization, Intelligent Transportation System, Cultural Algorithm, Imperialist Competitive Algorithm, Smart City, Artificial Intelligence, Python, Evolutionary Algorithm, Traffic Simulation, Urban Mobility, Data Visualization, Transportation Research

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Core Features

Cultural Algorithm (CA)

• Optimize public transportation hub locations
• Simulate belief space evolution
• Improve traffic flow efficiency

Imperialist Competitive Algorithm (ICA)

• Allocate buses across transportation routes
• Balance urban traffic systems
• Simulate imperial competition behavior

Traffic Simulation

• Urban traffic modeling
• Transportation network optimization
• Real-world inspired traffic structures

Data Visualization

• Optimization process visualization
• Evolution tracking across generations
• Traffic analysis charts and simulations

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Technology Stack

Technology	Description
Python	Main programming language
NumPy	Numerical computation
Pandas	Data analysis
Matplotlib	Data visualization
Jupyter Notebook	Experiment environment

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Project Structure

CA-ICA-Traffic-Optimization
├── data
├── models
├── ca_main.py
├── ica_main.py
├── README.md
└── requirements.txt

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Installation Guide

Requirements

• Python 3.x
• NumPy
• Pandas
• Matplotlib

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Installation

1. Clone Repository

git clone https://github.com/your-username/ca-ica-traffic-optimization.git
cd ca-ica-traffic-optimization

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2. Install Dependencies

pip install -r requirements.txt

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Run Project

Run Cultural Algorithm

python ca_main.py

Run Imperialist Competitive Algorithm

python ica_main.py

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Use Cases

CA-ICA Traffic Optimization is suitable for:

• Smart city research
• Transportation optimization
• AI & evolutionary algorithm experiments
• Traffic simulation systems
• Academic research projects
• Urban mobility analysis

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Future Improvements

Upcoming planned features:

• Real-time traffic data integration
• AI-based traffic prediction
• Smart signal optimization
• Multi-city traffic simulation
• Deep learning traffic analysis

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Contributing

Contributions are welcome.

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push your branch
5. Open a Pull Request

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Author

• Bùi Mạnh Hưng

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License

MIT License

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Built with ❤️ using Python & Evolutionary Algorithms