Segmentations and alternative specific specification

We consider 4 specifications for the constants:

• Not segmented

• Segmented by GA (yearly subscription to public transport)

• Segmented by luggage

• Segmented both by GA and luggage

We consider 6 specifications for the time coefficients:

• Generic and not segmented

• Generic and segmented with first class

• Generic and segmented with trip purpose

• Alternative specific and not segmented

• Alternative specific and segmented with first class

• Alternative specific and segmented with trip purpose

We consider 2 specifications for the cost coefficients:

• Generic

• Alternative specific

We obtain a total of 48 specifications. See Bierlaire and Ortelli (2023).

Michel Bierlaire, EPFL Sun Apr 27 2025, 15:54:33

import numpy as np
from IPython.core.display_functions import display

from biogeme.biogeme import BIOGEME
from biogeme.catalog import generic_alt_specific_catalogs, segmentation_catalogs
from biogeme.data.swissmetro 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,
    read_data,
)
from biogeme.expressions import Beta
from biogeme.models import loglogit
from biogeme.results_processing import compile_estimation_results, pareto_optimal

Read the data

database = read_data()

Definition of the segmentations.

segmentation_ga = database.generate_segmentation(
    variable='GA', mapping={0: 'noGA', 1: 'GA'}
)

segmentation_luggage = database.generate_segmentation(
    variable='LUGGAGE', mapping={0: 'no_lugg', 1: 'one_lugg', 3: 'several_lugg'}
)

segmentation_first = database.generate_segmentation(
    variable='FIRST', mapping={0: '2nd_class', 1: '1st_class'}
)

We consider two trip purposes: ‘commuters’ and anything else. We need to define a binary variable first.

database.dataframe['COMMUTERS'] = np.where(database.dataframe['PURPOSE'] == 1, 1, 0)

segmentation_purpose = database.generate_segmentation(
    variable='COMMUTERS', mapping={0: 'non_commuters', 1: 'commuters'}
)

Parameters to be estimated.

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

Catalogs for the alternative specific constants.

asc_train_catalog, asc_car_catalog = segmentation_catalogs(
    generic_name='asc',
    beta_parameters=[asc_train, asc_car],
    potential_segmentations=(
        segmentation_ga,
        segmentation_luggage,
    ),
    maximum_number=2,
)

Catalog for the travel time coefficient. Note that the function returns a list of catalogs. Here, the list contains only one of them. This is why there is a comma after “B_TIME_catalog”.

(b_time_catalog_dict,) = generic_alt_specific_catalogs(
    generic_name='b_time',
    beta_parameters=[b_time],
    alternatives=('train', 'swissmetro', 'car'),
    potential_segmentations=(
        segmentation_first,
        segmentation_purpose,
    ),
    maximum_number=1,
)

Catalog for the travel cost coefficient.

(b_cost_catalog_dict,) = generic_alt_specific_catalogs(
    generic_name='b_cost',
    beta_parameters=[b_cost],
    alternatives=('train', 'swissmetro', 'car'),
)

Definition of the utility functions.

v_train = (
    asc_train_catalog
    + b_time_catalog_dict['train'] * TRAIN_TT_SCALED
    + b_cost_catalog_dict['train'] * TRAIN_COST_SCALED
)
v_swissmetro = (
    b_time_catalog_dict['swissmetro'] * SM_TT_SCALED
    + b_cost_catalog_dict['swissmetro'] * SM_COST_SCALED
)
v_car = (
    asc_car_catalog
    + b_time_catalog_dict['car'] * CAR_TT_SCALED
    + b_cost_catalog_dict['car'] * CAR_CO_SCALED
)

Associate utility functions with the numbering of alternatives.

v = {1: v_train, 2: v_swissmetro, 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, generate_html=False, generate_yaml=False
)
the_biogeme.model_name = 'b05alt_spec_segmentation'

Estimate the parameters.

dict_of_results = the_biogeme.estimate_catalog()

Number of estimated models.

print(f'A total of {len(dict_of_results)} models have been estimated')

All estimation results

compiled_results, specs = compile_estimation_results(
    dict_of_results, use_short_names=True
)

display('All estimated models')
display(compiled_results)

Glossary

for short_name, spec in specs.items():
    print(f'{short_name}\t{spec}')

Estimation results of the Pareto optimal models.

pareto_results = pareto_optimal(dict_of_results)
compiled_pareto_results, pareto_specs = compile_estimation_results(
    pareto_results, use_short_names=True
)

display('Non dominated models')
display(compiled_pareto_results)

Glossary.

for short_name, spec in pareto_specs.items():
    print(f'{short_name}\t{spec}')