London Bus Time Prediction

A machine learning-based system for predicting bus arrival times in London, UK. This project uses a Bidirectional LSTM neural network to provide accurate predictions for various bus routes.

🚀 Quick Start (Frontend Only)

1. Clone the repository:

git clone https://github.com/EthanPisani/WDS_LTC_BUS.git
cd WDS_LTC_BUS/frontend

2. Install dependencies:

pip install -r requirements.txt

3. Launch the frontend:

streamlit run frontend.py

Access the app at http://localhost:8501 in your browser.

---

🐳 Docker (Full Stack)

To run both backend and frontend services:

1. From the root of the repository:

docker-compose up --build

2. Services:

• Backend at http://localhost:5240
• Frontend at http://localhost:5241

---

📚 Project Overview

This project consists of two main components:

1. Backend API

   • Built with Flask
   • Handles model predictions
   • Provides route and stop information
   • Runs on http://localhost:5240

2. Frontend Application

   • Built with Streamlit
   • Interactive UI for bus time predictions
   • Runs on http://localhost:5241

---

🛠️ Project Structure

WDS_LTC_BUS/
├── backend/               # Flask app and model files
│   ├── app.py
│   ├── Model.py
│   ├── best_model.pth
│   ├── requirements.txt
│   └── ...
├── frontend/              # Streamlit app
│   ├── frontend.py
│   ├── requirements.txt
│   └── ...
├── docker-compose.yml     # Multi-container setup
└── README.md

---

📊 Features

• Real-time bus arrival predictions
• Interactive route/stop selector
• Model performance visualizations
• Mobile-responsive Streamlit frontend

---

🛠️ Technologies Used

• Machine Learning: PyTorch, Scikit-learn, Pandas, NumPy
• Web Frameworks: Flask (API), Streamlit (Frontend)
• Visualization: Matplotlib, Plotly

---

🤝 Contributing

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

---

📝 License

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

👥 Authors

• Ethan Pisani
• Henrique Leite
• Hadi Youssef
• Marc Alex Crasto
• Mohannad Salem
• Mollo Hou
• Riley Wong
• Saad Naeem