Choice of Travelling mode between Sidney and Melbourne
GREENE CH 19 "ECONOMETRIC ANALYSIS" 5TH ED Table
F21.2: Data Used to Study Travel Mode Choice (1997)
The R file contains data about revealed preferences of mode choice on 210 individuals. Original
data is in wide format, but RData is in long type format with 840 registers: alternative specific
information for the 4 competing modes is available for the 210 individuals. The alternatives are:
Air, Train, Bus, or Car - Ref. Car
The variables are:
1.
2.
3.
4.
5.
6.
7.
mode = choice; Air, Train, Bus, or Car - Ref. Car
ttme = terminal waiting time, 0 for car
invc = in vehicle cost - cost component,
invt = travel time, in vehicle,
gcost = generalized cost measure,
hinc = household income,
psize = party size in mode chosen.
1. Load R Workspace. Summarize data. Later, define a nest variable for each observation, for
HLogit, modes are specified to be ground alternatives (2,3,4) in one nest and fly alternative (1)
in another nest. Randomly split the sample in two sets: Working Set for estimation purposes
and Test Set (60-40 or 70-30).
a. Be careful to check in the long format data.frame that variables are properly defined.
b. Define a dummy variable ground containing 1 for all registers belonging to ground
alternatives alternatives.
c. Define a dummy variable iflyinc containing individual home’s income for the flying
alternative and 0 for the rest (interaction between individual variable and flying
alternative, i.e. exogenous income variable would be included as a generic/specific
alternative variable only for flying alternative).
d. Define a dummy variable ipsizeinc containing individual home’s income for car
alternative and 0 for non car alternatives (interaction between individual variable and
car alternative, i.e. exogenous income variable would be included as a
generic/specific alternative variable only for car alternative).
2. Formulate and discuss partworths in a simple MNL modelwith/ without alternative specific
constants.
a. Compute null model (with constants in the utilities). Write systematic utility for each
alternative.
b. Compute the pure conditional multinomial logit with generic coefficients for invehicle
time and in vehicle cost (invt, invc): with/without alternative specific constants. Write
systematic utility for each alternative.
c. Check partworths consistency and statistical significance. Compute predictive capacity
and check market share in model prediction and actual data.
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d. Compute wish to pay, i.e. extra money $AUS to pay to save one unit of in vehicle
travel time.
e. Compute manually the alternative specific constants if alternative 2 were used as the
reference one. Check with mlogit results.
3. House income is going to be considered. Formulate, compute and discuss 2/3 possibilities
containing as generic alternative variables invt and invc. Write systematic utility for each
alternative. Which one do you prefer?
4. Introduce waiting time at the terminal as alternative variable. Is it worth to use alternative
specific coefficients or statistically equivalent to propose a generic coefficient options? Write
systematic utility for each alternative.
5. Introduce psize of the group as individual variable considered in all alternatives or just in a
subset. Is it worth statistically? Write systematic utility for each alternative.
6. Some authors (Simon Jackman, Models for Unordered Outcomes, Univ. Standford, 2003)
discuss a model with alternative variables generalized cost and waiting time at the terminal
and house income for flying alternative. Compute, discuss and compare this model with those
proposed in previous points. Write systematic utility for each alternative and compute
prediction rate for Working Set and Test Set.
7. Try an MNL heterocedástic option considering alternative variables generalized cost and
waiting time at the terminal and house income for flying alternative.
8. Compute a nested logit model considering two nodes: flying and ground nodes. Write
alternative utilities and check consistency in each case: a) a common log sum parameter is
considered for the two nests b) node specific log –sum parameters c) Check if log –sum
parameter/s (depending on previous results) is significatively different from 1 d) Select the
best proposal available at this point.
9. Compute a nested logit model considering three nodes: flying, car and bus-train nodes. Write
alternative utilities and check consistency in each case: a) a common log sum parameter is
considered for the three nests b) node specific log –sum parameters c) Check if log –sum
parameter/s (depending on previous results) is significatively different from 1 d) Select the
best proposal available at this point.
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