Travel Mode Choice Data

Description

Data on travel mode choice for travel between Sydney and Melbourne, Australia.

Usage

data("TravelMode")

Format

A data frame containing 840 observations on 4 modes for 210 individuals.

individual

Factor indicating individual with levels 1 to 200.

mode

Factor indicating travel mode with levels "car", "air", "train", or "bus".

choice

Factor indicating choice with levels "no" and "yes".

wait

Terminal waiting time, 0 for car.

vcost

Vehicle cost component.

travel

Travel time in the vehicle.

gcost

Generalized cost measure.

income

Household income.

size

Party size.

Source

Online complements to Greene (2003).

http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

See Also

Greene2003

Examples

data("TravelMode")

## overall proportions for chosen mode
with(TravelMode, prop.table(table(mode[choice == "yes"])))

## travel vs. waiting time for different travel modes
library("lattice")
xyplot(travel ~ wait | mode, data = TravelMode)

## Greene (2003), Table 21.11, conditional logit model
if(require("mlogit")) {
TravelMode$incair <- with(TravelMode, income * (mode == "air"))
tm_cl <- mlogit(choice ~ gcost + wait + incair, data = TravelMode,
  shape = "long", alt.var = "mode", reflevel = "car")
summary(tm_cl)
}

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(AER)
Loading required package: car
Loading required package: lmtest
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AER/TravelMode.Rd_%03d_medium.png", width=480, height=480)
> ### Name: TravelMode
> ### Title: Travel Mode Choice Data
> ### Aliases: TravelMode
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data("TravelMode")
> 
> ## overall proportions for chosen mode
> with(TravelMode, prop.table(table(mode[choice == "yes"])))

      air     train       bus       car 
0.2761905 0.3000000 0.1428571 0.2809524 
> 
> ## travel vs. waiting time for different travel modes
> library("lattice")
> xyplot(travel ~ wait | mode, data = TravelMode)
> 
> ## Greene (2003), Table 21.11, conditional logit model
> if(require("mlogit")) {
+ TravelMode$incair <- with(TravelMode, income * (mode == "air"))
+ tm_cl <- mlogit(choice ~ gcost + wait + incair, data = TravelMode,
+   shape = "long", alt.var = "mode", reflevel = "car")
+ summary(tm_cl)
+ }
Loading required package: mlogit
Loading required package: Formula
Loading required package: maxLik
Loading required package: miscTools

Please cite the 'maxLik' package as:
Henningsen, Arne and Toomet, Ott (2011). maxLik: A package for maximum likelihood estimation in R. Computational Statistics 26(3), 443-458. DOI 10.1007/s00180-010-0217-1.

If you have questions, suggestions, or comments regarding the 'maxLik' package, please use a forum or 'tracker' at maxLik's R-Forge site:
https://r-forge.r-project.org/projects/maxlik/

Call:
mlogit(formula = choice ~ gcost + wait + incair, data = TravelMode, 
    reflevel = "car", shape = "long", alt.var = "mode", method = "nr", 
    print.level = 0)

Frequencies of alternatives:
    car     air   train     bus 
0.28095 0.27619 0.30000 0.14286 

nr method
5 iterations, 0h:0m:0s 
g'(-H)^-1g = 0.000234 
successive function values within tolerance limits 

Coefficients :
                   Estimate Std. Error t-value  Pr(>|t|)    
air:(intercept)    5.207433   0.779055  6.6843 2.320e-11 ***
train:(intercept)  3.869036   0.443127  8.7312 < 2.2e-16 ***
bus:(intercept)    3.163190   0.450266  7.0252 2.138e-12 ***
gcost             -0.015501   0.004408 -3.5167  0.000437 ***
wait              -0.096125   0.010440 -9.2075 < 2.2e-16 ***
incair             0.013287   0.010262  1.2947  0.195414    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Log-Likelihood: -199.13
McFadden R^2:  0.29825 
Likelihood ratio test : chisq = 169.26 (p.value = < 2.22e-16)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>