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--- |
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sdk: streamlit |
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app_file: app.py |
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--- |
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# TrafCast |
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A traffic speed prediction system for Los Angeles using LSTM neural networks. |
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## 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. |
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## Model Details |
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- **Architecture**: LSTM neural network with 2,191,617 parameters |
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- **Training Data**: 32+ million data points from LA traffic sensors |
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- **Performance**: Best validation loss of 6.6276, test loss of 6.0229 |
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- **Features**: Weather data, road characteristics, time patterns, and historical speeds |
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## Quick Start |
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### Prerequisites |
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- Python 3.8+ |
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- Virtual environment (recommended) |
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### Installation |
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1. **Clone the repository** |
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```bash |
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git clone <repository-url> |
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cd TrafCast |
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``` |
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2. **Create and activate virtual environment** |
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```bash |
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python -m venv .venv |
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source .venv/bin/activate # On Windows: .venv\Scripts\activate |
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``` |
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3. **Install dependencies** |
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```bash |
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pip install -r requirements.txt |
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``` |
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4. **Run the application** |
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```bash |
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streamlit run app.py |
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``` |
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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 |
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2. Choose a date and time for prediction |
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3. Select visualization mode (Predicted, Real, or Comparison) |
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4. Click "Apply Prediction" to generate traffic speed maps |
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## 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. |
