Citi Bike Data Analysis & Strategic Dashboard
- Jeffrey Frankenfeld
- Mar 21
- 3 min read
Updated: Apr 28
Background
Citi Bike has become an essential part of New York City's transportation network, offering a sustainable and convenient way to navigate the city. But with increasing demand, especially since the pandemic, availability issues have grown more frequent. Riders often encounter empty docks at busy stations or find it difficult to return bikes when docks are full.
This project explores how user behavior, weather, and geography impact Citi Bike's performance, and presents an interactive dashboard to help stakeholders make data-informed decisions about bike distribution and station planning.
Goals
Uncover usage patterns and rider behavior through time-series and geographic analysis
Identify what's causing bike availability issues by analyzing trends across seasons, stations, and user types
Develop data-driven insights to support smarter bike redistribution and infrastructure planning
Build an interactive dashboard that helps both technical and non-technical stakeholders explore trends and take action
Support growth and efficiency by providing actionable recommendations for expanding the Citi Bike system
Rider Behavior and Patterns
Understanding how and when people use Citi Bike is key to solving distribution issues.
Temperature and Ridership
My analysis showed that bike ridership rises sharply with warmer temperatures, peaking during the summer months and declining significantly in colder weather.
Time-of-Day Usage Patterns
My analysis revealed that weekday trips spike during traditional commute hours (8am and 5-7pm), while weekend usage is more evenly spread, peaking in the late morning and afternoon. Highlighting the contrast between commuter and recreational behavior.
High-Demand Stations
Seasonality matters: Ride patterns change dramatically by time of year
Top stations remain consistent, but rider purposes vary - commuters dominate months, while tourists drive warm-season traffic
(In the interactive dashboard, users can filter by season to explore how station rankings change over time)
Trip Distribution Across New York City
Understanding where Citi Bike trips begin and end across the city is critical for optimizing station placement and bike availability. Using an interactive map, I visualized aggregated trip flows to highlight major commuter corridors, recreational hotspots, and areas of opportunity.
Central Park and Roosevelt Island emerge as recreational trip hotspots
Dense commuter corridors are visible in Midtown and Chelsea
Overlapping start and end points indicate common round-trip routes
Outer boroughs show opportunity areas for expansion based on lower trip density
Strategic Recommendations
Based on my analysis of rider patterns, seasonal trends, and station demand, I developed the following data-driven strategies to improve Citi Bike's operational efficiency and rider experience:
Optimize Seasonal Bike Deployment
Increase bike availability near parks and tourist hotspots during spring and summer
Prioritize commuter-heavy stations in fall and winter to align with work travel patterns
Improve Availability at High-Demand Locations
Expand docking capacity near Central Park and Roosevelt Island to meet peak leisure demand
Ensure full docks are available at major transit hubs during weekday rush hours
Enhance Real-Time Bike Management
Leverage live data tracking to monitor high-demand stations and predict shortages
Improve redistribution logistics to minimize station overcrowding and bike shortages
Encourage Year-Round Ridership
Offer seasonal promotions or discounts to incentivize winter ridership
Invest in infrastructure improvements like heated docking stations and helmet rentals to support cold-weather cycling
Final Thoughts
This project allowed me to deepen my skills in data integration, advanced visualization, and dashboard development while tackling a real-world operational challenge. By analyzing rider behavior patterns, mapping trip density across New York City, and building an interactive dashboard, I developed actionable strategies to help optimize Citi Bike's bike distribution and expansion planning.
What I Would Improve
Refine the dashboard aesthetics to enhance visual appeal
Incorporate more advanced interactivity, such as dynamic date range selectors and custom station analysis
Deliverables
Explore my full case study, interact with the live dashboard, or dive into the code and datasets below: