MAEDtravel: Trip dataset

In nmm: Nonlinear Multivariate Models

Description

Data gathered in Austria in 2015 according to Mobility-Activity-Expenditure-Dairy (MAED), which reported all trips, activities (time use) and expenditures of 737 persons over a whole week.

Usage

data(MAEDtravel)

Format

A dataframe containing:

PeID

individual index

PeGenF

gender of the individual

PeAge

age in years

PeEduc

education level

PeEmploy

employment state

HhCh

type of household: with children or without children

WeID

trip index

choice

chosen mode: 1 - walk, 2 - bike, 3 - car, 4 -public transport (PT)

dist

trip distance, km

avl_1

availability dummy for mode 1, walk

avl_2

availability dummy for mode 2, bike

avl_3

availability dummy for mode 3, car

avl_4

availability dummy for mode 4, PT

chc_1

choice dummy for mode 1, walk

chc_2

choice dummy for mode 2, bike

chc_3

choice dummy for mode 3, car

chc_4

choice dummy for mode 4, PT

cost_3

cost of car mode

cost_4

cost of PT mode

dur_1

trip duration with mode 1, minutes

dur_2

trip duration with mode 2, minutes

dur_3

trip duration with mode 3, minutes

vdur_4

in vehicle time mode 4, minutes

acc_4

time to stop or from stop for mode 4, minutes

HhCarPark

dummy, car parking at home available

JobCarPark

dummy,car parking at workplace available

PbAvl_3

dummy, ar parking restrictions (time and/or cost) in-force at the destination of the trip

servIdx_4

public transport service interval in minutes

stopUs1R1_4

necessary number of changes to reach the destination with public transport

leis

trip purpose leisure, effect coding

work

trip purpose work, effect coding

oth

trip purpose other, effect coding

int_1

inertia for mode 1

int_2

inertia for mode 2

int_3

inertia for mode 3

int_4

inertia for mode 4

Details

For more on data collection and description see \insertCiteaschauer2015nmm and \insertCiteaschauer2018nmm.

A variant of this dataset was used in: \insertCiteschmid2017nmm, \insertCitejokubauskaite2018nmm and \insertCitehoessinger2018nmm.

To get the full dataset please contact [e-mail].

Transport modes available: walk, bike, car, public transport (PT). The inertia variable (int_i) is a dummy, which is equal to one if the mode chosen by a person for a trip at the start of the current tour is the same as the one chosen in the previous tour made for the same purpose, and zero otherwise. Variables for trip purpose (leis, work, oth) were created using the effect coding.

References

\insertRef

aschauer2018nmm

\insertRef

aschauer2015nmm

\insertRef

hoessinger2018nmm

\insertRef

jokubauskaite2018nmm

\insertRef

schmid2017nmm

Examples

data(MAEDtravel)

nmm documentation built on Jan. 7, 2021, 5:10 p.m.