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CITI BIKE ANALYSIS

Overview

OBJECTIVE 

 

Examine the root causes of distribution challenges facing Citi Bike in New York to develop practical solutions.​

TOOLS 

STREAMLIT.png

SKILLS

​​​​

  • Descriptive data analysis

  • Geospatial data visualization

  • Interactive dashboard creation with Streamlit

  • Data integration and transformation using Python (Pandas, Matplotlib, Seaborn)

  • API data retrieval

​​

CITI BIKE

Contains bike ride IDs, bike types, trip timings, station details, and

station coordinates.

 

NOAA

Contains the average temperature for each day in 2022.​

DATA

Analysis

Seasonal Trend

I explored how bike trip frequency varied throughout the year, investigating whether certain months experienced higher usage and if weather conditions influenced these patterns.

dualtemp.png

KEY INSIGHT: Bike trips peak during the warmer months (May to October) and reach their lowest during the colder months (November to April). Similarly, temperatures are highest in summer and lowest in winter. This insight suggests that the shortage issue Citi Bike faces is primarily a concern during the warmer months.

Busiest Times of the Week

I also aimed to identify peak usage times during the week.

heatmap.png

KEY INSIGHT: Wednesdays, Thursdays, and Fridays emerge as the busiest days of the week, with a notable surge in rider activity around 4-6 pm.

Most Popular Bike Routes

Finally, I analyzed which areas/routes had the highest popularity.

KEPLERMAP copy.png

KEY INSIGHT: The most popular routes are near the Hudson River Greenway, Chelsea, and the Meatpacking District.

Recommendations

  • Add more stations or increase the size of existing stations in high-demand areas to accomodate bike shortages, such as the Hudson River Greenway, Chelsea, and the Meatpacking District.

  • Offer incentives, such as a $2 ride credit for users who return bikes to less busy stations or ride during off-peak hours, particularly on Wednesdays, Thursdays, and Fridays around 4-6 pm.

  • Based on the weekly demand forecast, we can ensure the stations in high-demand areas are adequately stocked with bikes during the warmer months, while simultaneously reducing the supply in winter and late autumn to lower logistics costs.

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