README.md

metadata

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

   git clone <repository-url>
   cd TrafCast
   

2. Create and activate virtual environment

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Run the application

   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.