CrashInsight - AI-Powered Traffic Accident Analytics 🚗📊

CrashInsight is an advanced web application that leverages artificial intelligence and machine learning to analyze traffic accident data, providing actionable insights for improving road safety.

🚀 Local Development Setup

Prerequisites

• Python 3.11+
• Node.js 18+
• npm or yarn

Backend Setup

# Navigate to backend directory
cd backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install Python dependencies
pip install -r requirements.txt

# Run the Flask server
python app.py

Backend will be available at: http://localhost:5000

Frontend Setup

# Navigate to project root
cd ..

# Install Node.js dependencies
npm install

# Start the development server
npm run dev:vite

Frontend will be available at: http://localhost:5173

Full Development Environment

1. Start backend: cd backend && python app.py
2. Start frontend: npm run dev:vite
3. Open http://localhost:5173 in your browser

🌟 Features

📈 Analytics Dashboard

• Real-time Statistics: Live accident counts, fatality rates, and injury metrics
• Interactive Charts: Hourly trends, daily patterns, and severity distributions
• Dark/Light Mode: Fully responsive design with theme switching

🤖 Machine Learning Insights

• Predictive Modeling: Random Forest classifier for crash severity prediction
• Feature Importance: Identify key factors contributing to accidents
• Model Performance: Real-time accuracy metrics and validation

🔍 Pattern Analysis

• Clustering Analysis: AI-powered accident pattern recognition
• Risk Assessment: Visual risk categorization (High/Medium/Low)
• Time-based Patterns: Rush hour, weekend, and seasonal trends

📋 Association Rules Mining

• Accident Causation: Discover relationships between conditions and outcomes
• Filtered Insights: Focus on accident-relevant patterns only
• Plain English: Human-readable rule interpretations

🚀 Tech Stack

Frontend

• React 18 + TypeScript
• Tailwind CSS for styling
• Framer Motion for animations
• Chart.js for data visualizations
• Vite for fast development

Backend

• Python Flask REST API
• Pandas & NumPy for data processing
• Scikit-learn for machine learning
• MLxtend for association rule mining

Data

• Chicago Traffic Accidents Dataset (209K+ records)
• Real-time API integration
• Comprehensive accident attributes

🛠️ Installation & Setup

Prerequisites

• Node.js 18+ and npm
• Python 3.8+ with pip
• Git for version control

1. Clone Repository

git clone https://github.com/Chirag8405/CrashInsight.git
cd CrashInsight

2. Frontend Setup

# Install dependencies
npm install

# Start development server
npm run dev

3. Backend Setup

# Navigate to backend
cd backend

# Install dependencies
pip install -r requirements.txt

# Start Flask server
python app.py

4. Access Application

• Frontend: http://localhost:5174
• Backend API: http://localhost:5000/api

🚀 Production Deployment

This application is designed for deployment with:

• Backend: Render.com (Python Flask with dataset)
• Frontend: Netlify (React build)

See RENDER_NETLIFY_DEPLOYMENT.md for detailed deployment instructions.

📁 Project Structure

CrashInsight/
├── src/                    # React frontend
│   ├── components/         # React components
│   ├── assets/            # Static assets
│   └── styles/            # CSS files
├── backend/               # Python Flask API
│   ├── app.py            # Main Flask application
│   └── traffic_accidents.csv  # Dataset
├── public/               # Public assets
└── README.md            # Project documentation

🔧 API Endpoints

Endpoint	Method	Description
/api/health	GET	Health check
/api/basic-stats	GET	Basic accident statistics
/api/time-analysis	GET	Time-based patterns
/api/location-analysis	GET	Location-based insights
/api/severity-analysis	GET	Severity distribution
/api/clustering	GET	Clustering analysis
/api/ml-model	GET	ML model training & results
/api/association-rules	GET	Association rule mining

🎯 Key Insights Discovered

Accident Patterns

• High-Risk Pattern: Evening rush hours on Fridays (avg 2.3+ injuries)
• Safest Pattern: Morning weekday accidents (avg 0.8 injuries)
• Peak Danger Time: 17:00-19:00 (6-7 PM)

Association Rules Examples

• Stop Sign + High Injury → Side-Impact Collision (67% confidence)
• Nighttime + Rear-End → Multi-Vehicle Accident (79% confidence)
• Clear Weather + Stop Sign → Angle Collision (66% confidence)

🚦 Safety Recommendations

Based on our analysis:

1. Enhanced Traffic Control at stop sign intersections during peak hours
2. Improved Lighting for nighttime accident prevention
3. Speed Reduction Measures in high-injury pattern areas
4. Driver Education about angle collision risks at intersections

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact

Chirag - @Chirag8405

Project Link: https://github.com/Chirag8405/CrashInsight

🙏 Acknowledgments

• Chicago Data Portal for accident dataset
• React + Chart.js communities
• Scikit-learn documentation
• MLxtend library for association rules

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