// File: 07discreteMixture.mod
// Author: Michel Bierlaire, EPFL
// Date: Sat Nov 13 11:25:16 2010
[ModelDescription]
"Example of a logit model for a transportation mode choice with 3 alternatives:"
"- Train"
"- Car"
"- Swissmetro, an hypothetical high-speed train"
"The time coefficient is assumed to be distributed. It is a discrete distribution with two mass points, one at 0, and one at B_TIME_OTHER. The probabilities associated with each mass point are W_0 and W_OTHER, respectively."
"Note that the model is unidentifiable. The objective of this example is to illustrate the Biogeme syntax only."
[Choice]
CHOICE
[Beta]
// Name Value LowerBound UpperBound status (0=variable, 1=fixed)
ASC_CAR 0 -10 10 0
ASC_TRAIN 0 -10 10 0
ASC_SM 0 -10 10 1
B_TIME_0 0 -10 10 1
W_0 0.5 0 1 0
B_TIME_OTHER 0 -10 10 0
W_OTHER 0.5 0 1 0
B_COST 0 -10 10 0
[LaTeX]
ASC_CAR "Cte. car"
ASC_SBB "Cte. train"
ASC_SM "Cte. Swissmetro"
B_TIME_0 "$\beta_\text{time,0}$"
B_TIME_OTHER "$\beta_\text{time,other}$"
W_0 "$\omega_0$"
W_OTHER "$\omega_\text{other}$"
B_COST "$\beta_\text{cost}$"
[DiscreteDistributions]
B_TIME < B_TIME_0 ( W_0 ) B_TIME_OTHER ( W_OTHER ) >
[LinearConstraints]
W_0 + W_OTHER = 1.0
[Utilities]
// Id Name Avail linear-in-parameter expression (beta1*x1 + beta2*x2 + ... )
1 A1_TRAIN TRAIN_AV_SP ASC_TRAIN * one
+ B_TIME * TRAIN_TT_SCALED
+ B_COST * TRAIN_COST_SCALED
2 A2_SM SM_AV ASC_SM * one
+ B_TIME * SM_TT_SCALED
+ B_COST * SM_COST_SCALED
3 A3_Car CAR_AV_SP ASC_CAR * one
+ B_TIME * CAR_TT_SCALED
+ B_COST * CAR_CO_SCALED
[Expressions]
// Define here arithmetic expressions for name that are not directly
// available from the data
one = 1
CAR_AV_SP = CAR_AV && ( SP != 0 )
TRAIN_AV_SP = TRAIN_AV && ( SP != 0 )
//If the person has a GA (season ticket) her incremental cost is actually 0
//rather than the cost value gathered from the
// network data.
SM_COST = SM_CO * ( GA == 0 )
TRAIN_COST = TRAIN_CO * ( GA == 0 )
// For numerical reasons, it is good practice to scale the data to
// that the values of the parameters are around 1.0.
// A previous estimation with the unscaled data has generated
// parameters around -0.01 for both cost and time. Therefore, time and
// cost are multipled my 0.01.
TRAIN_TT_SCALED = TRAIN_TT / 100.0
TRAIN_COST_SCALED = TRAIN_COST / 100
SM_TT_SCALED = SM_TT / 100.0
SM_COST_SCALED = SM_COST / 100
CAR_TT_SCALED = CAR_TT / 100
CAR_CO_SCALED = CAR_CO / 100
[Exclude]
// All observations verifying the following expression will not be
// considered for estimation
// The modeler here has developed the model only for work trips.
// Observations such that the dependent variable CHOICE is 0 are also removed.
(( PURPOSE != 1 ) * ( PURPOSE != 3 ) || ( CHOICE == 0 ))
[Model]
// $MNL stands for "multinomial logit model",
$MNL