Bike-Sharing-Washington-DC
OpenML dataset with id 43486
No author found.
Full work available at URL: https://api.openml.org/data/v1/download/22102311/Bike-Sharing-Washington-DC.arff
Upload date: 23 March 2022
Copyright license: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Dataset Characteristics
Number of features: 29 (numeric: 28, symbolic: 0 and in total binary: 0 )
Number of instances: 2,922
Number of instances with missing values: 2,922
Number of missing values: 51,510
Context Climate change is forcing cities to re-imaging their transportation infrastructure. Shared mobility concepts, such as car sharing, bike sharing or scooter sharing become more and more popular. And if they are implemented well, they can actually contribute to mitigating climate change. Bike sharing in particular is interesting because no electricity of gasoline is necessary (unless e-bikes are used) for this mode of transportation. However, there are inherent problems to this type of shared mobility:
varying demand at bike sharing stations needs to be balanced to avoid oversupply or shortages heavily used bikes break down more often
Forecasting the future demand can help address those issues. Moreover, demand forecasts can help operators decide whether to expand the business, determine adequate prices and generate additional income through advertisements at particularly busy stations. But that's not all. Another challenge is redistributing bikes between stations and determining the optimal routes. And determining the location of new stations is also an area of interest for operators. Content This dataset can be used to forecast demand to avoid oversupply and shortages. It spans from January 1, 2011, until December 31, 2018. Determining new station locations, analyzing movement patterns or planning routes will only be possible with additional data.
date - date with the format yyyy-mm-dd temp_avg - average daily temperature in degree Celsius temp_min - minimum daily temperature in degree Celsius temp_max - maximum daily temperature in degree Celsius temp_observ - temperature at the time of observation in degree Celsius precip - amount of precipitation in mm wind - wind speed in meters per second wt_fog - weather type fog, ice fog, or freezing fog (may include heavy fog) wtheavyfog - weather type heavy fog or heaving freezing fog (not always distinguished from fog) wt_thunder - weather type thunder wt_sleet - weather type ice pellets, sleet, snow pellets, or small hail wt_hail - weather type hail (may include small hail) wt_glaze - weather type glaze or rime wt_haze - weather type smoke or haze wtdriftsnow - weather type blowing or drifting snow wthighwind - weather type high or damaging winds wt_mist - weather type mist wt_drizzle - weather type drizzle wt_rain - weather type rain (may include freezing rain, drizzle, and freezing drizzle) wtfreezerain - weather type freezing rain wt_snow - weather type snow, snow pellets, snow grains, or ice crystals wtgroundfog - weather type ground fog wticefog - weather type ice fog or freezing fog wtfreezedrizzle - weather type freezing drizzle wt_unknown - weather type unknown source of precipitation casual - number of unregistered customers registered - number of registered customers total_cust - sum of registered and casual customers holiday - indicates whether the day is a holiday or not
Acknowledgements The data I used to create this dataset was taken from:
Capital Bikeshare for the bike sharing demand, NOAA's National Climatic Data Center for weather data, DC Department of Human Resources for data on public holidays.
Inspiration Think about the following questions/topics and add more data to this dataset to improve your results:
What will tomorrow's, next week's or next month's bike demand? Use time series analysis to determine this.
Use anomaly detection to identify seasonality and trend in daily customers data.
Which features are particularly important for the forecast of the bike demand?
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