NYC Citi Bike Supply Optimization
Data-Driven Analysis for Bike Share Operational Efficiency
Phase 1
Data Analysis
Phase 2
Dashboard Development
Phase 3
Deployment & Presentation
Project Synopsis
Business Objective: To optimize NYC Citi Bike's fleet management and station operations by analyzing usage patterns, reducing customer complaints about bike availability, and improving operational efficiency.
Core Question: "How can we predict demand fluctuations and optimize bike distribution across 1,500+ stations to maximize availability during peak hours and seasons?"
Key Deliverable: An interactive operational dashboard with demand forecasting, station performance analytics, and seasonal scaling recommendations.
My Role & Process:
- Data Analysis: Processed 2021-2022 ridership data to identify usage patterns and seasonal trends
- Correlation Analysis: Identified strong temperature-usage correlation (r=0.82) driving seasonal demand
- Station Analysis: Mapped top 20 stations handling disproportionate trip volumes
- Dashboard Development: Built interactive Streamlit dashboard for stakeholder decision-making
Weather Impact & Seasonal Patterns
Temperature Correlation
Dual-axis analysis showing strong relationship between temperature and daily bike trips.
Key Finding: 0.82 correlation between temperature and usage, with summer peaks reaching 140,000 trips vs winter lows of 45,000.
Seasonal Temperature Patterns
NYC temperature fluctuations throughout 2022 showing clear seasonal cycles.
Key Finding: Temperature ranges from -10°C to 30°C, driving the 60% seasonal variation in bike usage.
Daily Trip Patterns
Procedural analysis of daily bike trip volumes throughout 2022.
Key Finding: Consistent weekend peaks and seasonal growth from winter to summer months.
Seasonal Strategy Insights
- Clear Correlation: 0.82 temperature-usage correlation confirms weather as primary demand driver
- Peak Season: May-October accounts for 70% of annual ridership with 140,000 daily trip peaks
- Winter Optimization: November-April shows 40-50% lower demand, ideal for maintenance and scaling back
- Revenue Opportunity: Dynamic pricing during peak summer months could increase revenue 20-25%
Station Performance & Geographic Distribution
Station Usage Concentration
Top 20 stations by trip volume showing significant usage concentration.
Key Finding: W 21 St & 6 Ave leads with 129,018 trips, while top 5 stations handle 3x average volume.
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Trip Duration Analysis
Distribution of Citi Bike trip durations throughout 2022.
Key Finding: Majority of trips under 30 minutes, indicating short-distance urban commuting patterns.
Operational Optimization Insights
- High-Demand Focus: Top 20 stations require prioritized maintenance and frequent rebalancing
- Geographic Clustering: Stations in Midtown Manhattan and tourist areas show highest utilization
- Expansion Opportunities: Residential neighborhoods and waterfront corridors represent growth areas
- Maintenance Scheduling: Winter months ideal for comprehensive station maintenance and upgrades
Key Findings & Strategic Impact:
Operational Insights & Strategic Impact
Critical Operational Findings:
- Weather-Driven Demand: 0.82 temperature-usage correlation enables accurate seasonal forecasting—summer peaks at 140K daily trips vs winter lows of 45K.
- Station Concentration Risk: Top 5 stations handle 3x average volume, creating bottlenecks during peak hours.
- Seasonal Cost Optimization: November-April shows 40-50% lower demand, presenting fleet scaling opportunities.
Operational & Financial Recommendations:
- For Operations Management: Implement seasonal fleet scaling—reduce active bikes by 40% Nov-Apr, redirecting resources to maintenance and station upgrades.
- For Station Optimization: Create priority rebalancing routes for top 20 high-demand stations, especially during 7-9 AM and 5-7 PM commuter peaks.
- For Revenue Strategy: Introduce peak-season dynamic pricing (May-Oct) with 15-20% price premiums during weekends and holidays, projected to increase annual revenue by 20-25%.
- For Customer Experience: Deploy predictive availability alerts in the mobile app during high-demand periods to manage expectations and reduce complaints.
Strategic Business Impact:
- Data-driven seasonal scaling strategy reducing operational costs by 30-40%
- Optimized resource allocation to high-demand stations improving customer satisfaction
- Identified expansion opportunities in underserved geographic areas
- Dynamic pricing model increasing potential revenue by 20-25%
- Predictive maintenance scheduling during low-demand seasons