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Historical Hourly Bike Rentals and Weather Patterns
Retail & Consumer Behavior
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About
Monitoring the hourly flow of urban cyclists reveals how modern transportation is shifting away from purely recreational use toward essential daily commuting. As the bike-share market projects significant revenue growth through 2027, understanding the intersection of rental demand and environmental conditions becomes a vital strategy for operational success. These records provide a detailed window into how weather fluctuations and calendar events influence public mobility, offering a foundational tool for optimising fleet distribution and managing the financial risks associated with high capital expenditure and ongoing depreciation.
Columns
- datetime: The specific date and hour for each recorded data point.
- count: The total number of bicycle rentals processed during the specific hour.
- holiday: A binary indicator denoting whether the hour falls on a recognised public holiday.
- workingday: A marker indicating if the date is a weekday that is not a holiday.
- temp: The ambient temperature recorded in Celsius.
- feels_like: The perceived temperature in Celsius, accounting for wind and humidity.
- temp_min: The minimum temperature recorded within the hour.
- temp_max: The maximum temperature reached within the hour.
- pressure: The atmospheric air pressure.
- humidity: The percentage of relative humidity in the air.
- wind_speed: The recorded velocity of the wind.
- wind_deg: The direction of the wind measured in degrees.
- rain_1h: The volume of precipitation recorded over the previous hour.
- snow_1h: The volume of snowfall measured over the previous hour.
- cloud_all: The percentage of total cloud cover.
- weather_main: Categorical labels for weather types, such as Rain, Snow, or Extreme conditions.
Distribution
The information is delivered in a CSV file titled
capitalbikeshare-complete.csv with a file size of approximately 2.55 MB. It contains 33,400 valid records, representing a 100% validity rate with no missing or mismatched entries for the primary variables. While the majority of the timeline is intact, records for April 2020 are absent as they were not provided by the original system operator. No future updates are expected for this specific collection.Usage
This resource is ideal for conducting regression analysis to predict rental demand based on meteorological patterns and calendar types. It is well-suited for building machine learning models that help bike-sharing companies maximise allocation efficiency and mitigate potential financial losses. Additionally, researchers can use these records to perform exploratory data analysis on the recovery of urban transit systems post-pandemic or to study how specific weather extremes influence the choice of bicycles as a primary mode of transport.
Coverage
The geographic scope is centered on the urban areas and campuses served by the Capital Bikeshare system, primarily located within Washington DC. Temporally, the records span from 1 January 2018 through to 31 August 2021. The demographic focus covers a wide range of users, from households using bikes for essential commuting to delivery industry workers seeking to avoid traffic congestion.
License
CC0: Public Domain
Who Can Use It
Data science students and beginners can leverage these records to practice time-series forecasting and multi-variable regression. Urban planners and transport authorities might utilise the data to better understand commuting patterns and the environmental factors that discourage or encourage cycling. Furthermore, business analysts at mobility startups can find this a valuable primary source for benchmarking operational efficiency against historical demand fluctuations.
Dataset Name Suggestions
- Capital Bike Share: Hourly Demand and Weather Trends (2018-2021)
- Washington DC Urban Mobility and Meteorological Archive
- Bike-Sharing Demand Prediction and Environmental Registry
- Historical Hourly Bike Rentals and Weather Patterns
- Capital Bikeshare Operational Efficiency and Demand Index
Attributes
Original Data Source:Historical Hourly Bike Rentals and Weather Patterns
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