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Seoul Hourly Bike Rental Data
Retail & Consumer Behavior
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About
Focuses on the public bicycle sharing movement in Seoul, South Korea, providing hourly metrics related to rental counts and associated environmental and temporal factors. The dataset is instrumental for understanding urban mobility patterns, assessing the influence of climate on transport use, and developing models for future bike-sharing demand in metropolitan areas. It captures key variables essential for analysing the development of Korea's mobility movement and the public engagement with shared cycling resources.
Columns
The dataset contains 14 distinct columns detailing date, bike counts, and meteorological conditions:
- Date: The date of observation (365 unique values).
- Rented Bike Count: The total number of bikes rented during the corresponding hour. Values range from 0 to 3,556, with a mean of approximately 705.
- Hour: The hour of the day (ranging from 0 to 23), with a mean of 11.5.
- Temperature(°C): Hourly temperature measurements, ranging from a minimum of -17.8°C to a maximum of 39.4°C (mean 12.9°C).
- Humidity(%): Percentage of humidity, ranging from 0% to 98% (mean 58.2%).
- Wind speed (m/s): Wind speed measured in metres per second, up to 7.4 m/s (mean 1.72 m/s).
- Visibility (10m): Visibility measurements, up to 2,000 metres (mean 1.44 kilometres).
- Dew point temperature(°C): Dew point temperature, ranging from -30.6°C to 27.2°C (mean 4.07°C).
- Solar Radiation (MJ/m2): Solar radiation measured in megajoules per square metre, up to 3.52 MJ/m2 (mean 0.57 MJ/m2).
- Rainfall(mm): Rainfall measurements in millimetres, up to 35 mm (mean 0.15 mm).
- Snowfall (cm): Snowfall measurements in centimetres, up to 8.8 cm (mean 0.08 cm).
- Seasons: Categorical data including Spring and Summer.
- Holiday: Binary indicator (Holiday or No Holiday).
- Functioning Day: Boolean status indicating if the bike sharing service was operational (97% of observations are True).
Distribution
The data is provided in a CSV file format named
SeoulBikeData.csv, totaling 604.17 kB. It features 14 columns with 8,760 total records across all columns, indicating a full year of hourly data. The data is structured with no missing values, mismatched data, or invalid entries. The expected update frequency for this data product is weekly.Usage
This data is perfectly suited for time-series forecasting of rental demand, exploratory data analysis focused on weather impacts on transport, and building predictive models. Ideal applications include:
- Developing strategies for optimising bike station supply and distribution based on hourly and seasonal factors.
- Analysing the correlation between adverse weather conditions (rain, snow, high temperatures) and bike usage decline.
- Supporting urban planning and policy proposals related to sustainable mobility infrastructure.
- Data visualization projects illustrating daily and seasonal trends in Seoul's public transport usage.
Coverage
The geographic scope is focused solely on Seoul, South Korea, detailing the bike-sharing system within the city. The data captures hourly metrics over an extended period (represented by 365 unique dates). The scope also includes specific factors like seasonal variation and whether the day was a holiday or a functioning operational day.
License
CC0: Public Domain
Who Can Use It
- Data Analysts and Scientists: For performing statistical analysis, machine learning model training, and time-series predictions of urban mobility.
- Urban Planners and Policy Makers: To inform decisions regarding public transport funding, infrastructure expansion, and developing proposals for increased bicycle use.
- Students and Researchers: For academic projects focused on transport economics, environmental influences on behaviour, and developing countries' mobility solutions.
Dataset Name Suggestions
- Seoul Hourly Bike Rental Data
- Seoul Bike Sharing Demand and Weather
- Korea Urban Mobility Metrics
- Seoul Bike Use and Climate Factors
- Hourly Seoul Bicycle Activity.
Attributes
Original Data Source: Seoul Hourly Bike Rental Data
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