EX5.BIKE {regclass}

BIKE dataset for Exercise 4 Chapter 5

Description

BIKE dataset for Exercise 4 Chapter 5

Usage

data("EX5.BIKE")

Format

A data frame with 413 observations on the following 9 variables.

Demand

a numeric vector

Day

a factor with levels Friday Monday Saturday Sunday Thursday Tuesday Wednesday

Workingday

a factor with levels no yes

Holiday

a factor with levels no yes

Weather

a factor with levels No rain Rain

AvgTemp

a numeric vector

EffectiveAvgTemp

a numeric vector

AvgHumidity

a numeric vector

AvgWindspeed

a numeric vector

Details

Adapted from the bike sharing dataset on the UCI data repository http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset. This concerns the demand for rental bikes in the DC area. This is an expanded version of EX4.BIKE with more variables and without the row containing bad data.

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.

References

Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.