New York City's Metropolitan Transportation Authority (MTA) Subway system, one of the largest and busiest in the world, operates over 400 stations with approximately 665 miles of track weaving through the city's boroughs. This project focuses on an in-depth analysis of the MTA's subway ridership patterns, specifically comparing the data from 2023 with historical trends to understand the impact of significant events such as the COVID-19 pandemic on ridership.
My analysis highlights the dramatic shifts in ridership, showcasing substantial decreases post-2020, with annual figures struggling to rebound to their pre-pandemic levels. To accomplish this, I developed a data pipeline capable of handling vast datasets sourced from official city government platforms. Key technologies and tools employed include:
- Python: Utilized extensive data cleaning and transformation processes to ensure accuracy and consistency between datasets.
- SQL Server: Employed for efficient data storage, management, and querying, allowing for seamless data preparation.
- Power BI: Used for creating comprehensive visualizations and reports to communicate insights and trends effectively.
This project illuminates how external factors have reshaped public transportation usage but also demonstrates the application of data engineering and analysis techniques to address real-world challenges.
(Note: According to past MTA reports, ridership is calculated by tracking all passengers who enter the subway system, including passengers who transfer from buses for free. It is important to note that the MTA combines ridership data for station complexes, where transfer passageways connect stations. Due to this, the MTA can't accurately allocate ridership to each station in a complex.)
Link to the interactive dashboard:
https://data.ny.gov/Transportation/MTA-Daily-Ridership-Data-Beginning-2020/vxuj-8kew/about_data
https://new.mta.info/agency/new-york-city-transit/subway-bus-ridership-2023