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

Event labeling combining ensemble detectors and background knowledge

By Hadi Fanaee-T, João Gama. 2013

Published in Progress in Artificial Intelligence

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
instantIDIntegerrecord indexno
dtedayFeatureDatedateno
seasonFeatureCategorical1:winter, 2:spring, 3:summer, 4:fallno
yrFeatureCategoricalyear (0: 2011, 1: 2012)no
mnthFeatureCategoricalmonth (1 to 12)no
hrFeatureCategoricalhour (0 to 23)no
holidayFeatureBinaryweather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)no
weekdayFeatureCategoricalday of the weekno
workingdayFeatureBinaryif day is neither weekend nor holiday is 1, otherwise is 0no
weathersitFeatureCategorical- 1: Clear, Few clouds, Partly cloudy, Partly cloudyno

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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

Papers Citing this Dataset

Recurrent Neural Networks for Time Series Forecasting

By G'abor Petneh'azi. 2019

Published in

VINE: Visualizing Statistical Interactions in Black Box Models

By Matthew Britton. 2019

Published in ArXiv.

Recurrent Neural Networks for Time Series Forecasting

By Gábor Petneházi. 2019

Published in ArXiv.

Anchor regression: heterogeneous data meets causality

By Dominik Rothenhausler, Nicolai Meinshausen, Peter Buhlmann, Jonas Peters. 2018

Published in

Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport

By Adarsh Subbaswamy, Peter Schulam, Suchi Saria. 2018

Published in AISTATS.

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12 citations
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Creators

Hadi Fanaee-T

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