m8nt0 / smartcitytrafficmanagement

Sophisticated Smart City Traffic Management System

Smart City Traffic Management

Welcome to the Smart City Traffic Management system. This project aims to leverage AI and machine learning techniques to optimize and manage traffic flow in a smart city environment.

Table of Contents

• Introduction
• Features
• Project Structure
• Setup and Installation
• Usage
• Configuration
• Development
• Testing
• Deployment
• Contributing
• License

Introduction

This project provides a comprehensive system to manage traffic in a smart city using various machine learning models and APIs. It includes functionalities such as data loading, preprocessing, model training, prediction, monitoring, and visualization through a web application.

Features

• Data Management: Load and preprocess traffic data.
• Machine Learning Models: Train and use supervised, unsupervised, and reinforcement learning models.
• API: Expose endpoints for predictions and data interactions.
• Monitoring: Track model performance and errors.
• Web Application: Visualize traffic data and model predictions on a dashboard.
• Security: Audit and scan for vulnerabilities.
• Continuous Integration/Continuous Deployment: Automated testing and deployment using GitHub Actions.

Project Structure

smart-city-traffic-management/
├── .env
├── .gitignore
├── .pre-commit-config.yaml
├── .github/
│   └── workflows/
│       ├── ci.yml
│       └── cd.yml
├── CHANGELOG.md
├── CONTRIBUTING.md
├── INSTALLATION.md
├── README.md
├── USAGE.md
├── configs/
│   ├── data_config.yaml
│   ├── deployment_config.yaml
│   ├── model_config.yaml
│   └── training_config.yaml
├── deployment/
│   ├── api/
│   │   ├── api_endpoints.go
│   │   ├── api_endpoints.py
│   │   └── requirements.txt
│   └── docker/
│       ├── Dockerfile
│       └── docker-compose.yml
├── docs/
│   ├── README.md
│   ├── CONTRIBUTING.md
│   ├── INSTALLATION.md
│   ├── USAGE.md
│   └── CHANGELOG.md
├── experiments/
│   ├── experiment_1.yaml
│   └── experiment_2.yaml
├── monitoring/
│   ├── logging_config.py
│   ├── error_tracking.py
│   ├── model_monitoring.py
│   └── performance_monitoring.py
├── notebooks/
│   ├── data_preprocessing.ipynb
│   ├── deep_learning.ipynb
│   ├── nlp_analysis.ipynb
│   ├── reinforcement_learning.ipynb
│   ├── supervised_learning.ipynb
│   ├── unsupervised_learning.ipynb
│   └── experiments/
│       └── experiment_analysis.ipynb
├── reports/
│   └── final_report.pdf
├── security/
│   ├── security_audit.py
│   └── vulnerability_scan.py
├── src/
│   ├── data/
│   │   ├── data_loader.py
│   │   ├── data_preprocessing.py
│   │   ├── feature_engineering.py
│   │   └── data_schema.json
│   ├── models/
│   │   ├── supervised/
│   │   │   └── regression_model.py
│   │   ├── unsupervised/
│   │   │   └── clustering_model.py
│   │   ├── reinforcement/
│   │   │   └── rl_agent.py
│   │   └── nlp/
│   │       └── nlp_model.py
│   ├── main.py
│   └── utils/
│       ├── helpers.py
│       └── validators.py
├── setup.py
├── requirements_dev.txt
└── tests/
    ├── test_api_endpoints.py
    ├── test_data_loading.py
    ├── test_integration.py
    ├── test_model_evaluation.R
    ├── test_model_evaluation.py
    ├── test_model_training.py
    └── test_end_to_end.py
├── webapp/
│   ├── static/
│   │   ├── app.js
│   │   ├── styles.css
│   │   └── charts.js
│   ├── templates/
│   │   ├── index.html
│   │   └── dashboard.html
│   └── app.py

Setup and Installation

Prerequisites

• Python 3.8 or higher
• Docker
• Git

Installation

1. Clone the repository:

   git clone https://github.com/your-username/smart-city-traffic-management.git
   cd smart-city-traffic-management

2. Create and activate a virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Setup environment variables:

   Create a .env file in the root directory and add your configurations (example provided).

5. Initialize the database:

   Ensure your database is set up as per configs/data_config.yaml.

6. Run migrations (if applicable):

   # Example for Flask-Migrate
   flask db upgrade

Usage

Running the API

1. Start the Flask API server:

   python deployment/api/api_endpoints.py

2. Access the API at http://localhost:8000.

Running the Web Application

1. Start the Flask web application:

   python webapp/app.py

2. Access the web application at http://localhost:5000.

Docker

1. Build and run the Docker container:

   docker-compose up --build

2. Access the services as configured.

Configuration

Configuration files are located in the configs/ directory.

• Data Configuration: configs/data_config.yaml
• Deployment Configuration: configs/deployment_config.yaml
• Model Configuration: configs/model_config.yaml
• Training Configuration: configs/training_config.yaml

Development

Setting Up Pre-commit Hooks

1. Install pre-commit:

   pip install pre-commit

2. Install the hooks:

   pre-commit install

Running Jupyter Notebooks

Jupyter notebooks for experimentation and analysis are located in the notebooks/ directory. To run them, start Jupyter:

```bash
jupyter notebook

Testing

Running Unit Tests

```bash
pytest

Running Security Audits

```bash
python security/security_audit.py
python security/vulnerability_scan.py

Deployment

Continuous Integration and Deployment

This project uses GitHub Actions for CI/CD. Workflows are defined in the .github/workflows/ directory.

• CI Workflow: .github/workflows/ci.yml
• CD Workflow: .github/workflows/cd.yml

Contributing

We welcome contributions! Please see the CONTRIBUTING.md for more details on how to get started.