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---
sdk: streamlit
app_file: app.py
---
# TrafCast
A traffic speed prediction system for Los Angeles using LSTM neural networks.
## Overview
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
- **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
```
The app will be available at `http://localhost:8501`
## Usage
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
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. |