Divvy 2019-2020 Ridership Analysis

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1 Executive Summary

• Ridership growth: Total rides across 2019 and 2020 were 788,189, with casual users accounting for 8.6% of rides, leaving room for membership growth.
• Ride duration: Casual riders average 89.5 minutes per trip, compared with 13.3 minutes for members.
• Peak usage: Both user groups peak on weekends, but casual ridership shows a sharper weekend spike, indicating leisure-oriented behavior.
• Top stations: The ten most-visited start stations for casual riders cluster near Chicago’s lakefront and tourist hot spots, suggesting strong visitor engagement.

2 Methodology

Data sources Divvy bike-share trip data for 2019 and 2020, downloaded as CSV files.

Data preparation steps

1. Standardized column names to the 2020 schema.
2. Stacked quarterly files, dropping deprecated latitude/longitude and demographic columns.
3. Cleaned member type labels and removed outlier/maintenance trips.
4. Added calendar fields (date, weekday) and calculated ride_length.

A complete, reproducible script is contained in the Appendix.

3 Key Findings

3.1 Ride Length Comparison

Average and Median Ride Duration by User Type

User Type	Average Duration	Median Duration	Ride Count
casual	89.5	23.2	67877
member	13.3	8.5	720312

3.2 Ride Volume by Weekday

3.3 Average Duration by Weekday

3.4 Ride Start-Time Distribution

3.5 Top Casual Rider Stations

4 Recommendations

1. Convert casual riders to members – promote discounted weekend membership plans at lakefront-adjacent stations.
2. Re-deploy capacity – add docks/bike capacity at top casual stations during summer weekends to meet demand spikes.
3. Targeted marketing – push in-app notifications highlighting scenic routes to casual riders during peak leisure hours.
4. Infrastructure partnerships – collaborate with the city to improve signage and bike lanes near tourist areas to further encourage casual ridership.

5 Next Steps

• Conduct similar analysis on post-2020 data to measure pandemic impact and recovery.
• Integrate weather data for a more nuanced demand forecast.
• Explore reintegrating location data into data collections for use in targeting specific locations.

Appendix: Full Reproducible Code

#  Set echo = TRUE so technical readers can review full script
#  (The code above in setup chunk is repeated here for transparency)