CitiBike Analytics
1M+ NYC bike-sharing trips analyzed with weather correlation, seasonal decomposition, and predictive demand modeling.
The Problem
NYC's CitiBike system generates over a million trips per year, but there's no accessible way to explore what drives ridership. Weather impact, seasonal patterns, and station-level demand are invisible to city planners and operators.
The Approach
Analysis of 1,051,000 trips across 365 days. Streamlit dashboard with 15+ chart types, 12+ interactive filters, weather correlation analysis, seasonal decomposition, and predictive demand forecasting. Kepler.gl for geospatial station mapping.
The non-obvious finding: temperature correlates with ridership at 0.768 — but the 56% variation based on weather conditions tells a more actionable story for operations planning than the raw correlation number.
The Result
Deployed at citibike2024.streamlit.app. 70% member vs. 30% casual split, station performance mapping, seasonal optimization strategies.
My Role
Solo. Full data pipeline — collection, cleaning, analysis, visualization, and deployment. 6 Jupyter notebooks for deep analysis, Streamlit app for interactive exploration.