PEDAT is a Streamlit-based web application designed to visualize pedestrian volume data in Utah. It offers an interactive and user-friendly interface to analyze and understand pedestrian traffic patterns effectively.
- Interactive Data Visualization: Utilizes libraries like Folium, Plotly, and Kepler.gl for dynamic and engaging data presentation.
- Comprehensive Data Analysis: Integrates with Pandas, and Google Cloud BigQuery for efficient data handling, manipulation, and analysis.
- Customizable Views: Offers various visualization options to cater to different analysis needs.
- Customizable Report: Ability to generate detailed reports for selected traffic signals and specific parameters, aiding in targeted analysis and decision-making.
- Python 3.6 or higher
- Required Python libraries:
- Streamlit >= 1.13.0
- Folium >= 0.13, <0.15
- Pandas, Plotly, Pydeck, DateTime, Matplotlib, NumPy
- Google Cloud BigQuery, Google Auth
- Other dependencies as listed in
requirements.txt
- Clone the repository:
git clone https://github.com/pozapas/PEDAT.gitcd PEDAT- Install dependencies:
pip install -r requirements.txt- Run the Streamlit app:
streamlit run dash_beta.pyIn the manual folder of the repository, you will find detailed guides to help you get started and make the most out of PEDAT:
- PEDAT User Guide: A comprehensive guide to using the PEDAT app, detailing all features and functionalities.
- How to Dockerize PEDAT App: Instructions on how to dockerize the PEDAT application for easy deployment and scalability.
- How to Install PEDAT App from Docker Hub: Step-by-step guide for installing the PEDAT app using the Docker image from Docker Hub.
PEDAT is also available as a Docker image. To use it:
- Pull the Docker image from Docker Hub:
docker pull pozapas/pedat- Run the Docker container:
docker run -p 8501:8501 pozapas/pedatContributions to PEDAT are welcome. Please read our guidelines and submit your pull requests or issues through GitHub.