Bike Sharing
Donated on 12/19/2013
This dataset contains the hourly and daily count of rental bikes between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information.
Dataset Characteristics
Multivariate
Subject Area
Social Science
Associated Tasks
Regression
Feature Type
Integer, Real
# Instances
17389
# Features
13
Dataset Information
Additional Information
Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. Hence, it is expected that most of important events in the city could be detected via monitoring these data.
Has Missing Values?
No
Introductory Paper
By Hadi Fanaee-T, João Gama. 2013
Published in Progress in Artificial Intelligence
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| instant | ID | Integer | record index | no | |
| dteday | Feature | Date | date | no | |
| season | Feature | Categorical | 1:winter, 2:spring, 3:summer, 4:fall | no | |
| yr | Feature | Categorical | year (0: 2011, 1: 2012) | no | |
| mnth | Feature | Categorical | month (1 to 12) | no | |
| hr | Feature | Categorical | hour (0 to 23) | no | |
| holiday | Feature | Binary | weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule) | no | |
| weekday | Feature | Categorical | day of the week | no | |
| workingday | Feature | Binary | if day is neither weekend nor holiday is 1, otherwise is 0 | no | |
| weathersit | Feature | Categorical | - 1: Clear, Few clouds, Partly cloudy, Partly cloudy | no |
0 to 10 of 17
Additional Variable Information
Both hour.csv and day.csv have the following fields, except hr which is not available in day.csv - instant: record index - dteday : date - season : season (1:winter, 2:spring, 3:summer, 4:fall) - yr : year (0: 2011, 1:2012) - mnth : month ( 1 to 12) - hr : hour (0 to 23) - holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule) - weekday : day of the week - workingday : if day is neither weekend nor holiday is 1, otherwise is 0. + weathersit : - 1: Clear, Few clouds, Partly cloudy, Partly cloudy - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog - temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale) - atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale) - hum: Normalized humidity. The values are divided to 100 (max) - windspeed: Normalized wind speed. The values are divided to 67 (max) - casual: count of casual users - registered: count of registered users - cnt: count of total rental bikes including both casual and registered
Dataset Files
| File | Size |
|---|---|
| hour.csv | 1.1 MB |
| day.csv | 56.2 KB |
| Readme.txt | 5.5 KB |
Papers Citing this Dataset
Sort by Year, desc
By Matthew Britton. 2019
Published in ArXiv.
By Dominik Rothenhausler, Nicolai Meinshausen, Peter Buhlmann, Jonas Peters. 2018
Published in
By Adarsh Subbaswamy, Peter Schulam, Suchi Saria. 2018
Published in AISTATS.
0 to 5 of 12
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset bike_sharing = fetch_ucirepo(id=275) # data (as pandas dataframes) X = bike_sharing.data.features y = bike_sharing.data.targets # metadata print(bike_sharing.metadata) # variable information print(bike_sharing.variables)
Fanaee-T, H. (2013). Bike Sharing [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5W894.
Creators
Hadi Fanaee-T
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.