Stated choices of intercity travel mode for 210 travellers, each choosing among the same four modes: air, train, bus and car. This is the classic Greene & Hensher (1997) data set, reshaped into choicer's long layout (one row per traveller-by-alternative). It is a convenient, recognizable example for multinomial and nested logit models and the demand/welfare toolkit (elasticities, diversion ratios, willingness-to-pay, counterfactuals).
Format
A data frame with 840 rows (210 travellers x 4 modes) and 9 columns:
- id
Integer traveller (choice situation) identifier, 1-210.
- mode
Factor giving the travel mode:
"air","train","bus"or"car". Use as the alternative column.- choice
Integer indicator, 1 for the chosen mode and 0 otherwise. Exactly one mode is chosen per traveller.
- wait
Terminal waiting time in minutes (0 for car).
- travel
In-vehicle travel time in minutes.
- vcost
In-vehicle cost component, in currency units.
- gcost
Generalized cost measure, in currency units.
- income
Household income (traveller level, in thousands).
- size
Size of the travelling party (traveller level).
Source
Greene, W. H. and Hensher, D. A. (1997). Reshaped from the TravelMode
data distributed with the AER package
(https://CRAN.R-project.org/package=AER). The same data appear in
Greene's Econometric Analysis and in several other choice-modelling
packages.
Details
wait, travel, vcost and gcost vary across modes
within a traveller, while income and size are traveller-level
attributes that are constant across modes. A standard specification regresses
the choice on wait, travel and vcost with
alternative-specific constants; vcost then plays the role of price for
willingness-to-pay and consumer-surplus calculations.
The sample is choice-based: the survey over-sampled the less popular
modes (air, train, bus) and under-sampled car, so sample choice shares do
not estimate population mode shares. With a full set of
alternative-specific constants the slope coefficients remain consistently
estimated under this design (Manski and Lerman, 1977), and
willingness-to-pay ratios are unaffected; the constants, and any shares,
elasticities or surplus levels computed from fitted probabilities, inherit
the sampling design. To target population quantities, attach WESML weights
with wesml_weights using external population shares; see
vignette("wesml", package = "choicer").
References
Manski, C. F. and Lerman, S. R. (1977). The estimation of choice probabilities from choice based samples. Econometrica, 45(8), 1977-1988.
Examples
data(mode_choice)
head(mode_choice)
#> id mode choice wait travel vcost gcost income size
#> 1 1 air 0 69 100 59 70 35 1
#> 2 1 train 0 34 372 31 71 35 1
#> 3 1 bus 0 35 417 25 70 35 1
#> 4 1 car 1 0 180 10 30 35 1
#> 5 2 air 0 64 68 58 68 30 2
#> 6 2 train 0 44 354 31 84 30 2
table(mode_choice$mode[mode_choice$choice == 1L])
#>
#> air train bus car
#> 58 63 30 59