Citi Bike Demand & User Analysis

Intro

I analyzed Citi Bike trip data from February 2026 to understand demand patterns, station usage, and rider behavior. Using Python, I cleaned and transformed raw data, then used SQL to answer key business questions around peak usage times and high-traffic locations. I built a Tableau dashboard to visualize trends and used AI to help generate structured insights and recommendations. The analysis highlights how demand fluctuates throughout the day, where usage is concentrated, and how member and casual riders behave differently.

Year

2026

Company

Citi Bike

"Citi Bike demand peaks around 5 PM, reflecting strong commuter patterns, and is concentrated in high-traffic Manhattan stations, led by W 21 St & 6 Ave. Members account for most rides, while casual users take longer trips (likely reflecting a mix of tourists and local leisure riders), suggesting that conversion opportunities should focus on identifying and targeting repeat local users rather than one-time visitors."

Final Thoughts

Personal Project

"Citi Bike demand peaks around 5 PM, reflecting strong commuter patterns, and is concentrated in high-traffic Manhattan stations, led by W 21 St & 6 Ave. Members account for most rides, while casual users take longer trips (likely reflecting a mix of tourists and local leisure riders), suggesting that conversion opportunities should focus on identifying and targeting repeat local users rather than one-time visitors."

Final Thoughts

Personal Project

Next work

Meta Equity Research