ServiceARC: AI-based Traffic Management System 🚗🚦

Welcome to ServiceARC, a state-of-the-art AI solution focused on optimizing traffic flow, enhancing road safety, and reducing traffic congestion. This system leverages the power of machine learning to predict emergency vehicle movements and estimate vehicle counts, paving the way for smarter and safer roads.

Features 🌟

• Optimized Traffic Flow: Streamline vehicle movement for efficient use of road networks.
• Enhanced Road Safety: Ensure that vehicles, pedestrians, and cyclists share the road securely.
• Reduced Congestion: Minimize traffic bottlenecks and ensure smoother flow.
• Emergency Vehicle Prediction: Predict the path and timings of emergency vehicles for effective road management.
• Vehicle Count Estimation: Employ machine learning models to estimate vehicle counts on various road sections.
• Emission Reduction: Achieves a commendable 20% emission reduction, contributing to a greener environment.

Technologies Used 🛠️

• Frontend: HTML, CSS, JavaScript
• Backend: Python (Machine Learning)

Team Members 🤝

• Shivansh: Algorithm Design

Results & Achievements 🏆

• Successfully implemented machine learning models to predict and manage traffic flow, leading to a 20% reduction in emissions.
• Enhanced overall traffic management efficiency for safer road travel experiences.

How to Set Up & Run 🔧

1. Clone this repository:

   git clone https://github.com/Shivansh46/ServiceArc.git
   cd ServiceArc

2. Setup a Virtual Environment (Optional but Recommended):

   This ensures that the dependencies of the project do not interfere with your global Python setup.

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

3. Install Required Dependencies:

   Make sure you have pip installed. Then, run:

   pip install -r requirements.txt

   Note: Ensure you have a requirements.txt in your repository with all necessary Python packages listed.

4. Setting up the Web Server:

   If you're using a framework or any server for your application, you'd have instructions to start it here. For instance, if you're using Flask:

   export FLASK_APP=app.py  # On Windows, use `set FLASK_APP=app.py`
   flask run

   Open your browser and navigate to http://127.0.0.1:5000/ to view the application.

5. Using the Machine Learning Models:

   Detailed instructions on how to train or use your pre-trained models for vehicle prediction and count estimation.

6. Frontend Setup:

   If there's any additional setup for your frontend, describe it here. For instance, if you have a frontend dev server:

   npm install
   npm start

7. Testing:

   Instructions for running tests if any are included:

   python -m unittest discover -s tests

8. Deactivate Virtual Environment:

   Once you're done, you can deactivate the virtual environment:

   deactivate

License 📄

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

Feedback & Contribution 💌

Feel free to fork this repository and submit pull requests. For any issues, suggestions, or feedback, open an issue or reach out to the author.