This project explores how New York City subway ridership patterns have changed between 2020 and 2024, with a specific focus on:
- 📈 Overall ridership recovery since the pandemic
- ⏰ Changes in peak commuting hours
- 🗺️ Differences in subway usage by borough and station
- 💳 The transition from MetroCard to OMNY
How has MTA subway ridership changed from 2020 to 2024, and have peak commuting hours shifted over time?
This project includes two components:
Python was used to fetch, clean, and analyze the hourly ridership dataset via the NYC Open Data API. Key visualizations were generated using matplotlib, seaborn, and pandas.
📎 View the Python Notebook here:
➡️ [MTA Transit 2020 - 2024.ipynb ](https://github.com/Juliarriibeiro/MTA-Transit/blob/45a6565360d22132ae4d32ddff0decfa30652a80/MTA%20Transit%202020%20-%202024.ipynb)
Tableau was used to create an interactive dashboard that complements the Python analysis and allows users to explore patterns by year, borough, station, and payment method.
📎 Explore the live dashboard on Tableau Public:
➡️ https://public.tableau.com/app/profile/julia.santos.ribeiro/viz/MTA-HowMTASubwayRidershipChangedfrom2020-2024/HowMTASubwayRidershipChanged20202024
- Stacked area chart of MetroCard vs OMNY usage by month
- Hourly ridership heatmap comparing peak hours across years
- Top 10 busiest stations ranked by total ridership
- Borough-level trends showing geographic ridership shifts
- Treemap & bubble charts to add interactivity and visual diversity
- Python: pandas, matplotlib, seaborn, requests
- Tableau: interactive dashboard design, filters, color legends
- GitHub: project hosting and documentation
Julia Ribeiro
Feel free to fork, star, or reach out if you have feedback or want to collaborate!