12 monthly data files containing raw trip records for the Divvy bike-sharing system throughout 2024. The dataset captures individual ride details including precise timestamps, station identifiers, and bike categories for urban mobility in Chicago.
Use Cases
- Calculate average trip duration and velocity by processing the started_at and ended_at timestamp columns.
- Identify high-traffic transit hubs using the start_station_id and end_station_id fields.
- Analyze seasonal demand fluctuations by aggregating records across the 12 monthly files.
- Compare the adoption and usage frequency of different vehicle technologies using the rideable_type attribute.
- Segment user behavior and trip frequency by filtering the member_casual categorical column.
Strengths
- Monthly granularity covering the complete 2024 calendar year.
- Includes rideable_type labels distinguishing between electric and classic bicycles.
- Contains started_at and ended_at timestamps for every individual trip record.
- Provides start_station_name and end_station_name for spatial network analysis.
- Categorizes users into member and casual riders via the member_casual column.