Vehicle Load Management System(NeuroCargo)

A web application to assess vehicle load safety and provide recommendations based on vehicle specifications, passenger count, and cargo weight.

Features

• Load Status Prediction: Determine if a vehicle is overloaded based on vehicle weight, passenger count, and cargo
• Risk Assessment: Get detailed risk analysis for vehicle loads
• Interactive Dashboard: Visual representation of load data with responsive UI
• Automatic Suggestions: Receive recommendations for safer load distribution
• Visualizations: View weight distribution through charts and graphs

Getting Started

Prerequisites

• Python 3.8+
• pip (Python package manager)
• Virtual environment (recommended)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/vehicle-load-management.git
   cd vehicle-load-management

2. Create and activate a virtual environment

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the application

   python app.py

5. Access the application

   • Open your browser and navigate to http://localhost:5000

Usage Guide

1. Select Vehicle Type

   • Choose from available vehicle types (2-wheeler, 4-wheeler, etc.)
   • Default specifications will be populated based on selection

2. Enter Vehicle Details

   • Vehicle Weight (kg)
   • Maximum Load Capacity (kg)
   • Number of Passengers
   • Cargo Weight (kg)

3. Check Load Status

   • Click "Check Load Status" to calculate and display results
   • View prediction result (Overloaded or Not Overloaded)
   • Review detailed metrics:
     • Load Percentage
     • Remaining Capacity
     • Risk Assessment
     • Total Weight
     • Fuel Efficiency Impact

4. Interpret Results

   • Green indicators: Safe load levels
   • Yellow indicators: Approaching maximum safe load
   • Red indicators: Unsafe load levels

Technical Information

System Architecture

• Backend: Flask (Python)
• Frontend: HTML, CSS, JavaScript (with Bootstrap 5)
• ML Component: Scikit-learn Random Forest model
• Data Visualization: Matplotlib

Model Information

The application uses a machine learning model to predict load status. If no existing model is found, a placeholder model is created automatically. The model considers:

• Vehicle type (one-hot encoded)
• Vehicle weight (scaled)
• Maximum load capacity (scaled)
• Passenger count (scaled)
• Cargo weight (scaled)

File Structure

vehicle-load-management/
├── app.py                  # Main Flask application
├── models/                 # Directory for ML models
│   ├── vehicle_load_model.pkl
│   └── vehicle_load_scaler.pkl
├── static/                 # Static files
│   ├── css/
│   │   └── style.css
│   └── images/
│       └── weight_distribution.png
├── templates/              # HTML templates
│   └── index.html
└── README.md

Customization

Adding New Vehicle Types

To add a new vehicle type:

1. Update the vehicle type dropdown in templates/index.html
2. Add appropriate weight/load defaults in the JavaScript section
3. If using a trained model, ensure it supports the new vehicle type

Extending Functionality

• Custom Risk Models: Modify the risk assessment logic in calculate_metrics() function
• Additional Metrics: Add new metrics to the returned dictionary in calculate_metrics()
• Enhanced Visualizations: Extend the generate_graphs() function to create more visualizations

Troubleshooting

Issue: Graphs not displaying

• Ensure the static/images directory exists and is writable
• Check browser console for any JavaScript errors

Issue: Model prediction errors

• Verify input data is in the correct format
• Check if the model file exists and is valid
• Look at the application logs for specific errors

License

This project is not licensed yet now.

Acknowledgments

• Bootstrap team for the responsive UI framework
• Scikit-learn contributors for the machine learning tools
• Flask team for the web framework

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For any questions or support, please open an issue on GitHub or contact the development team.