| --- |
| sdk: streamlit |
| app_file: app.py |
| --- |
| |
| # TrafCast |
|
|
| A traffic speed prediction system for Los Angeles using LSTM neural networks. |
|
|
| ## Overview |
|
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| TrafCast predicts real-time traffic speeds across major Los Angeles highways and roads using deep learning. The system uses an LSTM (Long Short-Term Memory) model trained on historical traffic data to forecast speed patterns. |
|
|
| ## Model Details |
|
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| - **Architecture**: LSTM neural network with 2,191,617 parameters |
| - **Training Data**: 32+ million data points from LA traffic sensors |
| - **Performance**: Best validation loss of 6.6276, test loss of 6.0229 |
| - **Features**: Weather data, road characteristics, time patterns, and historical speeds |
|
|
| ## Quick Start |
|
|
| ### Prerequisites |
| - Python 3.8+ |
| - Virtual environment (recommended) |
|
|
| ### Installation |
|
|
| 1. **Clone the repository** |
| ```bash |
| git clone <repository-url> |
| cd TrafCast |
| ``` |
|
|
| 2. **Create and activate virtual environment** |
| ```bash |
| python -m venv .venv |
| source .venv/bin/activate # On Windows: .venv\Scripts\activate |
| ``` |
|
|
| 3. **Install dependencies** |
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| 4. **Run the application** |
| ```bash |
| streamlit run app.py |
| ``` |
|
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| The app will be available at `http://localhost:8501` |
|
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| ## Usage |
|
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| 1. Select roads from the available LA highways |
| 2. Choose a date and time for prediction |
| 3. Select visualization mode (Predicted, Real, or Comparison) |
| 4. Click "Apply Prediction" to generate traffic speed maps |
|
|
| ## Data |
|
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| The model was trained on compressed CSV files containing traffic sensor data from major LA roads including I-405, US-101, I-5, and state highways. |
