Estimation of a logit model

Three alternatives:

• train,

• car and,

• Swissmetro.

Stated preferences data.

Michel Bierlaire, EPFL Wed Jun 18 2025, 09:56:59

import biogeme.biogeme_logging as blog
from IPython.core.display_functions import display
from biogeme.biogeme import BIOGEME
from biogeme.expressions import Beta
from biogeme.models import loglogit
from biogeme.results_processing import get_pandas_estimated_parameters

See the data processing script: Data preparation for Swissmetro.

from swissmetro_data import (
    CAR_AV_SP,
    CAR_CO_SCALED,
    CAR_TT_SCALED,
    CHOICE,
    SM_AV,
    SM_COST_SCALED,
    SM_TT_SCALED,
    TRAIN_AV_SP,
    TRAIN_COST_SCALED,
    TRAIN_TT_SCALED,
    database,
)

The logger sets the verbosity of Biogeme. By default, Biogem eis quite silent and generates only warnings. To have more information about what it happening behind the scene, the level should be set to blog.INFO.

logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b01logit_bis.py')

Example b01logit_bis.py

Parameters to be estimated: alternative specific constants

asc_car = Beta('asc_car', 0, None, None, 0)
asc_train = Beta('asc_train', 0, None, None, 0)

The constant associated with Swissmetro is normalized to zero. It does not need to be defined at all. Here, we illustrate the fact that setting the last argument of the Beta function to 1 fixes the parameter to its default value (here, 0).

asc_sm = Beta('asc_sm', 0, None, None, 1)

Coefficients of the attributes

b_time = Beta('b_time', 0, None, None, 0)
b_cost = Beta('b_cost', 0, None, None, 0)

Definition of the utility functions.

v_train = asc_train + b_time * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED
v_sm = asc_sm + b_time * SM_TT_SCALED + b_cost * SM_COST_SCALED
v_car = asc_car + b_time * CAR_TT_SCALED + b_cost * CAR_CO_SCALED

Associate utility functions with the numbering of alternatives.

v = {1: v_train, 2: v_sm, 3: v_car}

Associate the availability conditions with the alternatives.

av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}

Definition of the model. This is the contribution of each observation to the log likelihood function.

log_probability = loglogit(v, av, CHOICE)

Create the Biogeme object.

the_biogeme = BIOGEME(database, log_probability)
the_biogeme.model_name = 'b01logit'

Biogeme parameters read from biogeme.toml.

Calculate the null log likelihood for reporting.

the_biogeme.calculate_null_loglikelihood(av)

-6964.662979192191

Estimate the parameters.

results = the_biogeme.estimate()

*** Initial values of the parameters are obtained from the file __b01logit.iter
Parameter values restored from __b01logit.iter
Starting values for the algorithm: {'asc_train': -0.7011872849436401, 'b_time': -1.2778589565196719, 'b_cost': -1.0837900371207714, 'asc_car': -0.15463267198926273}
As the model is not too complex, we activate the calculation of second derivatives. To change this behavior, modify the algorithm to "simple_bounds" in the TOML file.
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Optimization algorithm has converged.
Relative gradient: 1.3841917136400373e-07
Cause of termination: Relative gradient = 1.4e-07 <= 6.1e-06
Number of function evaluations: 1
Number of gradient evaluations: 1
Number of hessian evaluations: 0
Algorithm: Newton with trust region for simple bound constraints
Number of iterations: 0
Optimization time: 0:00:00.135849
Calculate second derivatives and BHHH
File b01logit~00.html has been generated.
File b01logit~00.yaml has been generated.

print(results.short_summary())

Results for model b01logit
Nbr of parameters:              4
Sample size:                    6768
Excluded data:                  3960
Null log likelihood:            -6964.663
Final log likelihood:           -5331.252
Likelihood ratio test (null):           3266.822
Rho square (null):                      0.235
Rho bar square (null):                  0.234
Akaike Information Criterion:   10670.5
Bayesian Information Criterion: 10697.78

Get the results in a pandas table

pandas_results = get_pandas_estimated_parameters(
    estimation_results=results,
)
display(pandas_results)

        Name     Value  Robust std err.  Robust t-stat.  Robust p-value
0  asc_train -0.701187         0.082562       -8.492857        0.000000
1     b_time -1.277859         0.104254      -12.257120        0.000000
2     b_cost -1.083790         0.068225      -15.885521        0.000000
3    asc_car -0.154633         0.058163       -2.658590        0.007847