print(
'Run first the script 05latentChoiceFull.py in order to generate the file '
'05latentChoiceFull.pickle.')
sys.exit()
betas = results.getBetaValues()
### Variables
# Piecewise linear definition of income
ScaledIncome = DefineVariable('ScaledIncome', CalculatedIncome / 1000,
database)
thresholds = [None, 4, 6, 8, 10, None]
formulaIncome = models.piecewiseFormula(ScaledIncome, thresholds, [
betas['beta_ScaledIncome_lessthan_4'], betas['beta_ScaledIncome_4_6'],
betas['beta_ScaledIncome_6_8'], betas['beta_ScaledIncome_8_10'],
betas['beta_ScaledIncome_10_more']
])
# Definition of other variables
age_65_more = DefineVariable('age_65_more', age >= 65, database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse', HouseType == 1, database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren', \
((FamilSitu == 3) + (FamilSitu == 4)) > 0, database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
### Coefficients
def run_estimation_2015_2020():
"""
:author: Antonin Danalet, based on the example '01logit.py' by Michel Bierlaire, EPFL, on biogeme.epfl.ch
A binary logit model on the possibility to work from home at least some times."""
# Read the data
data_file_directory = Path('../data/output/data/estimation/2015_2020/')
df = pd.read_csv(data_file_directory / 'persons.csv', ';')
database = db.Database('persons', df)
# The following statement allows you to use the names of the variable as Python variable.
globals().update(database.variables)
# Parameters to be estimated
alternative_specific_constant = Beta('alternative_specific_constant', 0,
None, None, 0)
b_no_post_school_education = Beta('b_no_post_school_education', 0, None,
None, 0)
b_secondary_education = Beta('b_secondary_education', 0, None, None, 0)
b_tertiary_education = Beta('b_tertiary_education', 0, None, None, 0)
b_male_2020 = Beta('b_male_2020', 0, None, None, 1)
b_single_household_2020 = Beta('b_single_household_2020', 0, None, None, 1)
b_couple_without_children_2015 = Beta('b_couple_without_children_2015', 0,
None, None, 0)
b_couple_without_children_2020 = Beta('b_couple_without_children_2020', 0,
None, None, 0)
b_couple_with_children_2020 = Beta('b_couple_with_children_2020', 0, None,
None, 1)
b_single_parent_with_children_2020 = Beta(
'b_single_parent_with_children_2020', 0, None, None, 1)
b_not_family_household_2020 = Beta('b_not_family_household_2020', 0, None,
None, 1)
b_public_transport_connection_quality_abc_home_2020 = Beta(
'b_public_transport_connection_quality_abc_home_2020', 0, None, None,
1)
b_public_transport_connection_quality_na_home_2015 = Beta(
'b_public_transport_connection_quality_na_home_2015', 0, None, None, 0)
b_public_transport_connection_quality_na_home_2020 = Beta(
'b_public_transport_connection_quality_na_home_2020', 0, None, None, 1)
b_public_transport_connection_quality_abcd_work_2020 = Beta(
'b_public_transport_connection_quality_abcd_work_2020', 0, None, None,
1)
b_urban_home_2020 = Beta('b_urban_home_2020', 0, None, None, 1)
b_rural_home_2020 = Beta('b_rural_home_2020', 0, None, None, 1)
b_intermediate_home_2020 = Beta('b_intermediate_home_2020', 0, None, None,
1)
b_urban_work_2020 = Beta('b_urban_work_2020', 0, None, None, 1)
b_rural_work_2020 = Beta('b_rural_work_2020', 0, None, None, 1)
b_intermediate_work_2020 = Beta('b_intermediate_work_2020', 0, None, None,
0)
b_home_work_distance = Beta('b_home_work_distance', 0, None, None, 0)
b_home_work_distance_zero = Beta('b_home_work_distance_zero', 0, None,
None, 0)
b_home_work_distance_na = Beta('b_home_work_distance_na', 0, None, None, 0)
b_business_sector_agriculture_2020 = Beta(
'b_business_sector_agriculture_2020', 0, None, None, 1)
b_business_sector_production = Beta('b_business_sector_production', 0,
None, None, 0)
b_business_sector_wholesale = Beta('b_business_sector_wholesale', 0, None,
None, 0)
b_business_sector_retail = Beta('b_business_sector_retail', 0, None, None,
0)
b_business_sector_gastronomy = Beta('b_business_sector_gastronomy', 0,
None, None, 0)
b_business_sector_finance = Beta('b_business_sector_finance', 0, None,
None, 0)
b_business_sector_services_fc_2020 = Beta(
'b_business_sector_services_fc_2020', 0, None, None, 1)
b_business_sector_other_services = Beta('b_business_sector_other_services',
0, None, None, 0)
b_business_sector_others = Beta('b_business_sector_others', 0, None, None,
0)
b_business_sector_non_movers = Beta('b_business_sector_non_movers', 0,
None, None, 0)
b_executives = Beta('b_executives', 0, None, None, 0)
b_german = Beta('b_german', 0, None, None, 0)
b_hh_income_na = Beta('b_hh_income_na', 0, None, None, 0)
b_hh_income_8000_or_less = Beta('b_hh_income_8000_or_less', 0, None, None,
0)
b_owning_a_general_abo = Beta('b_owning_a_general_abo', 0, None, None, 0)
b_regional_abo_2020 = Beta('b_regional_abo_2020', 0, None, None, 1)
b_regional_abo_na_2020 = Beta('b_regional_abo_na_2020', 0, None, None, 1)
b_half_fare_abo_2020 = Beta('b_half_fare_abo_2020', 0, None, None, 1)
b_half_fare_abo_na_2020 = Beta('b_half_fare_abo_na_2020', 0, None, None, 1)
b_car_avail_2020 = Beta('b_car_avail_2020', 0, None, None, 1)
b_car_avail_na_2020 = Beta('b_car_avail_na_2020', 0, None, None, 1)
b_mobility_resource_na = Beta('b_mobility_resource_na', 0, None, None, 0)
b_mobility_resource_car_general_abo_2020 = Beta(
'b_mobility_resource_car_general_abo_2020', 0, None, None, 1)
b_mobility_resource_car_half_fare_abo = Beta(
'b_mobility_resource_car_half_fare_abo', 0, None, None, 0)
b_mobility_resource_car_2020 = Beta('b_mobility_resource_car_2020', 0,
None, None, 1)
b_mobility_resource_general_abo_no_car_2020 = Beta(
'b_mobility_resource_general_no_car_abo_2020', 0, None, None, 0)
b_mobility_resource_half_fare_abo_2020 = Beta(
'b_mobility_resource_half_fare_abo_2020', 0, None, None, 1)
b_mobility_resource_none_2020 = Beta('b_mobility_resource_none_2020', 0,
None, None, 1)
b_mobility_resource_car_half_fare_regional_abo_2020 = Beta(
'b_mobility_resource_car_half_fare_regional_abo_2020', 0, None, None,
1)
b_mobility_resource_car_regional_abo_2020 = Beta(
'b_mobility_resource_car_regional_abo_2020', 0, None, None, 1)
b_mobility_resource_half_fare_regional_abo_2020 = Beta(
'b_mobility_resource_half_fare_regional_abo_2020', 0, None, None, 1)
b_mobility_resource_regional_abo_2020 = Beta(
'b_mobility_resource_regional_abo_2020', 0, None, None, 1)
scale_2020 = Beta('scale_2020', 1, 0.001, None, 0)
''' Definition of new variables '''
male_2020 = DefineVariable('male', (sex == 1) * (year == 2020), database)
single_household_2020 = DefineVariable('single_household_2020',
(hh_type == 10) * (year == 2020),
database)
couple_without_children_2015 = DefineVariable(
'couple_without_children_2015', (hh_type == 210) * (year == 2015),
database)
couple_without_children_2020 = DefineVariable(
'couple_without_children_2020', (hh_type == 210) * (year == 2020),
database)
couple_with_children_2020 = DefineVariable(
'couple_with_children_2020', (hh_type == 220) * (year == 2020),
database)
single_parent_with_children_2020 = DefineVariable(
'single_parent_with_children_2020', (hh_type == 230) * (year == 2020),
database)
not_family_household_2020 = DefineVariable(
'not_family_household_2020', (hh_type == 30) * (year == 2020),
database)
public_transport_connection_quality_abc_home_2020 = \
DefineVariable('public_transport_connection_quality_abc_home_2020',
((public_transport_connection_quality_ARE_home == 1) +
(public_transport_connection_quality_ARE_home == 2) +
(public_transport_connection_quality_ARE_home == 3)) * (year == 2020), database)
public_transport_connection_quality_na_home_2015 = \
DefineVariable('public_transport_connection_quality_NA_home_2015',
(public_transport_connection_quality_ARE_home == 5) * (year == 2015), database)
public_transport_connection_quality_na_home_2020 = \
DefineVariable('public_transport_connection_quality_NA_home_2020',
(public_transport_connection_quality_ARE_home == 5) * (year == 2020), database)
public_transport_connection_quality_abcd_work_2020 = \
DefineVariable('public_transport_connection_quality_abc_work_2020',
((public_transport_connection_quality_ARE_work == 1) +
(public_transport_connection_quality_ARE_work == 2) +
(public_transport_connection_quality_ARE_work == 3) +
(public_transport_connection_quality_ARE_work == 4)) * (year == 2020), database)
urban_home_2020 = DefineVariable(
'urban_home_2020', (urban_typology_home == 1) * (year == 2020),
database)
rural_home_2020 = DefineVariable(
'rural_home_2020', (urban_typology_home == 3) * (year == 2020),
database)
intermediate_home_2020 = DefineVariable(
'intermediate_home_2020', (urban_typology_home == 2) * (year == 2020),
database)
urban_work_2020 = DefineVariable(
'urban_work_2020', (urban_typology_work == 1) * (year == 2020),
database)
rural_work_2020 = DefineVariable(
'rural_work_2020', (urban_typology_work == 3) * (year == 2020),
database)
intermediate_work_2020 = DefineVariable(
'intermediate_work_2020', (urban_typology_work == 2) * (year == 2020),
database)
home_work_distance = DefineVariable(
'home_work_distance',
home_work_crow_fly_distance * (home_work_crow_fly_distance >= 0.0) /
100000.0, database)
home_work_distance_zero = DefineVariable(
'home_work_distance_zero', home_work_crow_fly_distance == 0.0,
database)
home_work_distance_na = DefineVariable('home_work_distance_na',
home_work_crow_fly_distance == -999,
database)
executives = DefineVariable('executives', work_position == 1, database)
german = DefineVariable('german', language == 1, database)
hh_income_na = DefineVariable('hh_income_na', hh_income < 0, database)
hh_income_8000_or_less = DefineVariable(
'hh_income_8000_or_less', (hh_income == 1) + (hh_income == 2) +
(hh_income == 3) + (hh_income == 4), database)
owning_a_general_abo = DefineVariable('owning_a_general_abo',
GA_ticket == 1, database)
regional_abo_2020 = DefineVariable('regional_abo_2020',
(Verbund_Abo == 1) * (year == 2020),
database)
half_fare_abo_2020 = DefineVariable('half_fare_abo_2020',
(halbtax_ticket == 1) * (year == 2020),
database)
car_avail_always_or_on_demand_2020 = DefineVariable(
'car_avail_always_or_on_demand_2020',
((car_avail == 1) + (car_avail == 2)) * (year == 2020), database)
regional_abo_na_2020 = DefineVariable('regional_abo_na_2020',
(Verbund_Abo < 0) * (year == 2020),
database)
half_fare_abo_na_2020 = DefineVariable(
'half_fare_abo_na_2020', (halbtax_ticket < 0) * (year == 2020),
database)
car_avail_na_2020 = DefineVariable('car_avail_na_2020',
(car_avail < 0) * (year == 2020),
database)
mobility_resource_na = DefineVariable('mobility_resource_na',
mobility_resources == -98, database)
mobility_resource_car_general_abo_2020 = DefineVariable(
'mobility_resource_car_general_abo_2020',
(mobility_resources == 1) * (year == 2020), database)
mobility_resource_car_half_fare_abo = DefineVariable(
'mobility_resource_car_half_fare_abo', mobility_resources == 2,
database)
mobility_resource_car_2020 = DefineVariable('mobility_resource_car_2020',
(mobility_resources == 3) *
(year == 2020), database)
mobility_resource_general_abo_no_car_2020 = DefineVariable(
'mobility_resource_general_abo_no_car_2020',
(mobility_resources == 4) * (year == 2020), database)
mobility_resource_half_fare_abo_2020 = DefineVariable(
'mobility_resource_half_fare_abo_2020',
(mobility_resources == 5) * (year == 2020), database)
mobility_resource_none_2020 = DefineVariable('mobility_resource_none_2020',
(mobility_resources == 6) *
(year == 2020), database)
mobility_resource_car_half_fare_regional_abo_2020 = \
DefineVariable('mobility_resource_car_half_fare_regional_abo_2020',
(mobility_resources == 20) * (year == 2020), database)
mobility_resource_car_regional_abo_2020 = DefineVariable(
'mobility_resource_car_regional_abo_2020',
(mobility_resources == 30) * (year == 2020), database)
mobility_resource_half_fare_regional_abo_2020 = DefineVariable(
'mobility_resource_half_fare_regional_abo_2020',
(mobility_resources == 50) * (year == 2020), database)
mobility_resource_regional_abo_2020 = DefineVariable(
'mobility_resource_regional_abo_2020',
(mobility_resources == 60) * (year == 2020), database)
business_sector_agriculture_2020 = DefineVariable(
'business_sector_agriculture_2020',
business_sector_agriculture * (year == 2020), database)
business_sector_services_fc_2020 = DefineVariable(
'business_sector_services_fc_2020',
business_sector_services_fc * (year == 2020), database)
# Utility
U = alternative_specific_constant + \
b_executives * executives + \
b_no_post_school_education * no_post_school_educ + \
b_secondary_education * secondary_education + \
b_tertiary_education * tertiary_education + \
b_couple_without_children_2015 * couple_without_children_2015 + \
b_couple_without_children_2020 * couple_without_children_2020 + \
b_public_transport_connection_quality_na_home_2015 * public_transport_connection_quality_na_home_2015 + \
b_public_transport_connection_quality_na_home_2020 * public_transport_connection_quality_na_home_2020 + \
b_home_work_distance * home_work_distance + \
b_home_work_distance_zero * home_work_distance_zero + \
b_home_work_distance_na * home_work_distance_na + \
models.piecewiseFormula(age, [15, 19, 31, 79, 85]) + \
b_business_sector_retail * business_sector_retail + \
b_business_sector_gastronomy * business_sector_gastronomy + \
b_business_sector_finance * business_sector_finance + \
b_business_sector_production * business_sector_production + \
b_business_sector_wholesale * business_sector_wholesale + \
b_business_sector_other_services * business_sector_other_services + \
b_business_sector_others * business_sector_others + \
b_business_sector_non_movers * business_sector_non_movers + \
b_german * german + \
models.piecewiseFormula(work_percentage, [0, 90, 101]) + \
b_hh_income_na * hh_income_na + \
b_hh_income_8000_or_less * hh_income_8000_or_less + \
b_owning_a_general_abo * owning_a_general_abo + \
b_mobility_resource_na * mobility_resource_na + \
b_mobility_resource_car_half_fare_abo * mobility_resource_car_half_fare_abo + \
b_male_2020 * male_2020 + \
b_single_household_2020 * single_household_2020 + \
b_couple_with_children_2020 * couple_with_children_2020 + \
b_single_parent_with_children_2020 * single_parent_with_children_2020 + \
b_not_family_household_2020 * not_family_household_2020 + \
b_public_transport_connection_quality_abc_home_2020 * public_transport_connection_quality_abc_home_2020 + \
b_public_transport_connection_quality_abcd_work_2020 * public_transport_connection_quality_abcd_work_2020 + \
b_urban_home_2020 * urban_home_2020 + \
b_rural_home_2020 * rural_home_2020 + \
b_intermediate_home_2020 * intermediate_home_2020 + \
b_urban_work_2020 * urban_work_2020 + \
b_rural_work_2020 * rural_work_2020 + \
b_intermediate_work_2020 * intermediate_work_2020 + \
b_business_sector_agriculture_2020 * business_sector_agriculture_2020 + \
b_business_sector_services_fc_2020 * business_sector_services_fc_2020 + \
b_regional_abo_2020 * regional_abo_2020 + \
b_regional_abo_na_2020 * regional_abo_na_2020 + \
b_half_fare_abo_2020 * half_fare_abo_2020 + \
b_half_fare_abo_na_2020 * half_fare_abo_na_2020 + \
b_car_avail_2020 * car_avail_always_or_on_demand_2020 + \
b_car_avail_na_2020 * car_avail_na_2020 + \
b_mobility_resource_car_general_abo_2020 * mobility_resource_car_general_abo_2020 + \
b_mobility_resource_car_2020 * mobility_resource_car_2020 + \
b_mobility_resource_general_abo_no_car_2020 * mobility_resource_general_abo_no_car_2020 + \
b_mobility_resource_half_fare_abo_2020 * mobility_resource_half_fare_abo_2020 + \
b_mobility_resource_none_2020 * mobility_resource_none_2020 + \
b_mobility_resource_car_half_fare_regional_abo_2020 * mobility_resource_car_half_fare_regional_abo_2020 + \
b_mobility_resource_car_regional_abo_2020 * mobility_resource_car_regional_abo_2020 + \
b_mobility_resource_half_fare_regional_abo_2020 * mobility_resource_half_fare_regional_abo_2020 + \
b_mobility_resource_regional_abo_2020 * mobility_resource_regional_abo_2020
U_no_telecommuting = 0
# Scale associated with 2020 is estimated
scale = (year == 2015) + (year == 2020) * scale_2020
# Associate utility functions with the numbering of alternatives
V = {
1: scale * U, # Yes or sometimes
0: U_no_telecommuting
} # No
av = {1: 1, 0: 1}
# Definition of the model. This is the contribution of each
# observation to the log likelihood function.
logprob = models.loglogit(
V,
av, # All alternatives are supposed to be always available
telecommuting) # Choice variable
# Change the working directory, so that biogeme writes in the correct folder
standard_directory = os.getcwd()
output_directory = '../data/output/models/estimation/2015_2020/'
os.chdir(output_directory)
# Create the Biogeme object
biogeme = bio.BIOGEME(database, logprob)
output_file_name = 'logit_telecommuting_2015_2020'
biogeme.modelName = output_file_name
# Estimate the parameters
results = biogeme.estimate()
# Get the results in LaTeX
results.writeLaTeX()
# Go back to the normal working directory
os.chdir(standard_directory)
df = pd.read_csv('optima.dat', sep='\t')
database = db.Database('optima', df)
# The following statement allows you to use the names of the variable
# as Python variable.
globals().update(database.variables)
# Exclude observations such that the chosen alternative is -1
database.remove(Choice == -1.0)
# Piecewise linear definition of income
ScaledIncome = DefineVariable('ScaledIncome', CalculatedIncome / 1000, database)
thresholds = [None, 4, 6, 8, 10, None]
formulaIncome = models.piecewiseFormula(ScaledIncome,
thresholds,
[0.0, 0.0, 0.0, 0.0, 0.0])
# Definition of other variables
age_65_more = DefineVariable('age_65_more', age >= 65, database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse', HouseType == 1, database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren', \
((FamilSitu == 3) + (FamilSitu == 4)) > 0, database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
# Parameters to be estimated
coef_intercept = Beta('coef_intercept', 0.0, None, None, 0)
def run_simulation(data_file_directory_for_simulation, data_file_name_for_simulation, output_directory_for_simulation,
betas, household_income_limit):
"""
:author: Antonin Danalet, based on the example '01logit_simul.py' by Michel Bierlaire, EPFL, on biogeme.epfl.ch
Simulation with a binary logit model. Two alternatives: work from home at least some times, or not."""
# Read the data
df_persons = pd.read_csv(data_file_directory_for_simulation / data_file_name_for_simulation, ';')
database = db.Database('persons', df_persons)
# The following statement allows you to use the names of the variable as Python variable.
globals().update(database.variables)
# Parameters to be estimated
alternative_specific_constant = Beta('alternative_specific_constant', 0, None, None, 0)
b_no_post_school_education = Beta('b_no_post_school_education', 0, None, None, 0)
b_secondary_education = Beta('b_secondary_education', 0, None, None, 0)
b_tertiary_education = Beta('b_tertiary_education', 0, None, None, 0)
b_university = Beta('b_university', 0, None, None, 1)
b_male = Beta('b_male', 0, None, None, 0)
b_public_transport_connection_quality_na_home = Beta('b_public_transport_connection_quality_na_home',
0, None, None, 0)
b_public_transport_connection_quality_a_work = Beta('b_public_transport_connection_quality_are_a_work',
0, None, None, 1)
b_rural_work = Beta('b_rural_work', 0, None, None, 0)
b_home_work_distance = Beta('b_home_work_distance', 0, None, None, 0)
b_business_sector_agriculture = Beta('b_business_sector_agriculture', 0, None, None, 0)
b_business_sector_production = Beta('b_business_sector_production', 0, None, None, 0)
b_business_sector_wholesale = Beta('b_business_sector_wholesale', 0, None, None, 1)
b_business_sector_retail = Beta('b_business_sector_retail', 0, None, None, 0)
b_business_sector_gastronomy = Beta('b_business_sector_gastronomy', 0, None, None, 0)
b_business_sector_finance = Beta('b_business_sector_finance', 0, None, None, 1)
b_business_sector_services_fc = Beta('b_business_sector_services_fc', 0, None, None, 0)
b_business_sector_other_services = Beta('b_business_sector_other_services', 0, None, None, 1)
b_business_sector_others = Beta('b_business_sector_others', 0, None, None, 1)
b_business_sector_non_movers = Beta('b_business_sector_non_movers', 0, None, None, 0)
b_executives = Beta('b_executives', 0, None, None, 0)
b_german = Beta('b_german', 0, None, None, 0)
b_hh_income_8000_or_less = Beta('b_hh_income_8000_or_less', 0, None, None, 0)
# Definition of new variables
no_post_school_educ = education == 1
secondary_education = education == 2
tertiary_education = education == 3
university = education == 4
male = (sex == 1)
public_transport_quality_NA_home = (public_transport_connection_quality_ARE_home == 5)
public_transport_quality_A_work = (public_transport_connection_quality_ARE_work == 1)
home_work_distance = (home_work_crow_fly_distance * (home_work_crow_fly_distance >= 0.0) / 100000.0)
business_sector_agriculture = type_1 == 1
business_sector_retail = type_1 == 4
business_sector_gastronomy = type_1 == 5
business_sector_finance = type_1 == 6
business_sector_production = type_1 == 2
business_sector_wholesale = type_1 == 3
business_sector_services_fC = type_1 == 7
business_sector_other_services = type_1 == 8
business_sector_others = type_1 == 9
business_sector_non_movers = type_1 == 10
german = language == 1
nationality_switzerland = nation == 0
nationality_germany_austria = nation == 1
nationality_italy_vatican = nation == 2
nationality_france_monaco_s_marino = nation == 3
nationality_northwestern_europe = nation == 4
nationality_eastern_europe = nation == 7
hh_income_8000_or_less = hh_income < household_income_limit
executives = (0 < position_in_bus) * (position_in_bus < 19)
rural_work = urban_rural_typology_work == 3
# Utility
utility_function_telecommuting = alternative_specific_constant + \
b_executives * executives + \
b_no_post_school_education * no_post_school_educ + \
b_secondary_education * secondary_education + \
b_tertiary_education * tertiary_education + \
b_university * university + \
b_male * male + \
b_public_transport_connection_quality_na_home * public_transport_quality_NA_home + \
b_public_transport_connection_quality_a_work * public_transport_quality_A_work + \
b_rural_work * rural_work + \
b_home_work_distance * home_work_distance + \
models.piecewiseFormula(age, [0, 20, 35, 75, 200]) + \
b_business_sector_agriculture * business_sector_agriculture + \
b_business_sector_retail * business_sector_retail + \
b_business_sector_gastronomy * business_sector_gastronomy + \
b_business_sector_finance * business_sector_finance + \
b_business_sector_production * business_sector_production + \
b_business_sector_wholesale * business_sector_wholesale + \
b_business_sector_services_fc * business_sector_services_fC + \
b_business_sector_other_services * business_sector_other_services + \
b_business_sector_others * business_sector_others + \
b_business_sector_non_movers * business_sector_non_movers + \
b_german * german + \
b_nationality_ch_germany_france_italy_nw_e * nationality_switzerland + \
b_nationality_ch_germany_france_italy_nw_e * nationality_germany_austria + \
b_nationality_ch_germany_france_italy_nw_e * nationality_italy_vatican + \
b_nationality_ch_germany_france_italy_nw_e * nationality_france_monaco_s_marino + \
b_nationality_ch_germany_france_italy_nw_e * nationality_northwestern_europe + \
b_nationality_ch_germany_france_italy_nw_e * nationality_eastern_europe + \
models.piecewiseFormula(work_percentage, [0, 90, 101]) + \
b_hh_income_8000_or_less * hh_income_8000_or_less
utility_function_no_telecommuting = 0
# Associate utility functions with the numbering of alternatives
utility_functions_with_numbering_of_alternatives = {1: utility_function_telecommuting, # Yes or sometimes
3: utility_function_no_telecommuting} # No
availability_conditions = {1: 1, # Always available
3: 1} # Always available
# The choice model is a logit, with availability conditions
prob_telecommuting = models.logit(utility_functions_with_numbering_of_alternatives, availability_conditions, 1)
prob_no_telecommuting = models.logit(utility_functions_with_numbering_of_alternatives, availability_conditions, 3)
simulate = {'Prob. telecommuting': prob_telecommuting,
'Prob. no telecommuting': prob_no_telecommuting}
# Create the Biogeme object
biogeme = bio.BIOGEME(database, simulate)
biogeme.modelName = 'logit_telecommuting_simul'
# Define level of verbosity
logger = msg.bioMessage()
# logger.setSilent()
logger.setWarning()
# logger.setGeneral()
# logger.setDetailed()
# Get the betas from the estimation (without corrections)
# path_to_estimation_folder = Path('../data/output/models/estimation/')
# if os.path.isfile(path_to_estimation_folder / 'logit_telecommuting~00.pickle'):
# raise Exception('There are several model outputs! Careful.')
# results = res.bioResults(pickleFile=path_to_estimation_folder / 'logit_telecommuting.pickle')
# betas_without_correction = results.getBetaValues()
# Change the working directory, so that biogeme writes in the correct folder, i.e., where this file is
standard_directory = os.getcwd()
os.chdir(output_directory_for_simulation)
results = biogeme.simulate(theBetaValues=betas)
# print(results.describe())
df_persons = pd.concat([df_persons, results], axis=1)
# Go back to the normal working directory
os.chdir(standard_directory)
# For unemployed people, fix probability of doing some home office to 0 (and probability of not doing to 1).
df_persons.loc[df_persons.employed == 0, 'Prob. telecommuting'] = 0.0 # Unemployed people
df_persons.loc[df_persons.employed == 0, 'Prob. no telecommuting'] = 1.0 # Unemployed people
df_persons.loc[df_persons.employed == -99, 'Prob. telecommuting'] = 0.0 # Other people
df_persons.loc[df_persons.employed == -99, 'Prob. no telecommuting'] = 1.0 # Other people
# By definition, apprentices don't work from home (because they were not asked in the MTMC)
df_persons.loc[df_persons.position_in_bus == 3, 'Prob. telecommuting'] = 0.0
df_persons.loc[df_persons.position_in_bus == 3, 'Prob. no telecommuting'] = 1.0
# Add a realisation of the probability
df_persons['random 0/1'] = np.random.rand(len(df_persons))
df_persons['telecommuting_model'] = np.where(df_persons['random 0/1'] < df_persons['Prob. telecommuting'], 1, 0)
del df_persons['random 0/1']
''' Save the file '''
data_file_name = 'persons_from_SynPop_with_probability_telecommuting.csv'
df_persons.to_csv(output_directory_for_simulation / data_file_name, sep=',', index=False)
pandas = pd.read_csv("optima.dat", sep='\t')
database = db.Database("optima", pandas)
globals().update(database.variables)
exclude = (Choice == -1.0)
database.remove(exclude)
# Piecewise linear definition of income
# Note the use of the biogeme implementation of min and max, to avoid
# ambiguity with the python native functions.
ScaledIncome = DefineVariable('ScaledIncome',\
CalculatedIncome / 1000,database)
formulaIncome = models.piecewiseFormula(ScaledIncome,
[None, 4, 6, 8, 10, None])
age_65_more = DefineVariable('age_65_more', age >= Numeric(65), database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse',\
HouseType == 1,database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren',\
((FamilSitu == 3)+(FamilSitu == 4)) > 0,database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
### Coefficients
coef_intercept = Beta('coef_intercept', 0.0, None, None, 0)
coef_age_65_more = Beta('coef_age_65_more', 0.0, None, None, 0)
pandas = pd.read_csv("optima.dat", sep='\t')
database = db.Database("optima", pandas)
globals().update(database.variables)
exclude = (Choice == -1.0)
database.remove(exclude)
### Variables
ScaledIncome = DefineVariable('ScaledIncome',\
CalculatedIncome / 1000,database)
formulaIncome = models.piecewiseFormula(
ScaledIncome, [None, 4, 6, 8, 10, None], [
-0.33669944071374086, 0.2177481824894475, -0.6224104823788724,
1.1592372281254595, -0.35530173883216076
])
age_65_more = DefineVariable('age_65_more', age >= Numeric(65), database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse',\
HouseType == 1,database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren',\
((FamilSitu == 3)+(FamilSitu == 4)) > 0,database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
ASC_CAR = Beta('ASC_CAR', 0.4118057751313065, None, None, 0)
pandas = pd.read_csv("optima.dat", sep='\t')
database = db.Database("optima", pandas)
globals().update(database.variables)
exclude = (Choice == -1.0)
database.remove(exclude)
### Variables
ScaledIncome = DefineVariable('ScaledIncome',\
CalculatedIncome / 1000,database)
formulaIncome = models.piecewiseFormula(
ScaledIncome, [None, 4, 6, 8, 10, None], [
0.08954209471304636, -0.2209233080453265, 0.2591889240542216,
-0.5227805784067027, 0.08430692986645968
])
age_65_more = DefineVariable('age_65_more', age >= Numeric(65), database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse',\
HouseType == 1,database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren',\
((FamilSitu == 3)+(FamilSitu == 4)) > 0,database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
### Coefficients
pandas = pd.read_csv("optima.dat", sep='\t')
database = db.Database("optima", pandas)
globals().update(database.variables)
exclude = (Choice == -1.0)
database.remove(exclude)
### Variables
ScaledIncome = DefineVariable('ScaledIncome',\
CalculatedIncome / 1000,database)
formulaIncome = models.piecewiseFormula(
ScaledIncome, [None, 4, 6, 8, 10, None], [
0.15012511354122204, -0.287364030650875, 0.3378758520316341,
-0.6808138631421441, 0.1191396581172788
])
age_65_more = DefineVariable('age_65_more', age >= Numeric(65), database)
moreThanOneCar = DefineVariable('moreThanOneCar', NbCar > 1, database)
moreThanOneBike = DefineVariable('moreThanOneBike', NbBicy > 1, database)
individualHouse = DefineVariable('individualHouse',\
HouseType == 1,database)
male = DefineVariable('male', Gender == 1, database)
haveChildren = DefineVariable('haveChildren',\
((FamilSitu == 3)+(FamilSitu == 4)) > 0,database)
haveGA = DefineVariable('haveGA', GenAbST == 1, database)
highEducation = DefineVariable('highEducation', Education >= 6, database)
### Coefficients
def apply_model_to_example(df_persons, betas, output_directory_for_simulation,
output_file_name):
"""
:author: Antonin Danalet, based on the example '01logit_simul.py' by Michel Bierlaire, EPFL, on biogeme.epfl.ch
Simulation with a binary logit model. Two alternatives: work from home at least some times, or not."""
# Read the data
database = db.Database('persons', df_persons)
# The following statement allows you to use the names of the variable as Python variable.
globals().update(database.variables)
# Parameters to be estimated
alternative_specific_constant = Beta('alternative_specific_constant', 0,
None, None, 0)
b_no_post_school_education = Beta('b_no_post_school_education', 0, None,
None, 0)
b_secondary_education = Beta('b_secondary_education', 0, None, None, 0)
b_tertiary_education = Beta('b_tertiary_education', 0, None, None, 0)
b_university = Beta('b_university', 0, None, None, 1)
b_male = Beta('b_male', 0, None, None, 0)
b_public_transport_connection_quality_are_a_home = Beta(
'b_public_transport_connection_quality_are_a_home', 0, None, None, 1)
b_public_transport_connection_quality_are_b_home = Beta(
'b_public_transport_connection_quality_are_b_home', 0, None, None, 1)
b_public_transport_connection_quality_are_c_home = Beta(
'b_public_transport_connection_quality_are_c_home', 0, None, None, 1)
b_public_transport_connection_quality_are_d_home = Beta(
'b_public_transport_connection_quality_are_d_home', 0, None, None, 1)
b_public_transport_connection_quality_are_na_home = Beta(
'b_public_transport_connection_quality_are_na_home', 0, None, None, 0)
b_urban_work = Beta('b_urban_work', 0, None, None, 1)
b_rural_work = Beta('b_rural_work', 0, None, None, 0)
b_intermediate_work = Beta('b_intermediate_work', 0, None, None, 1)
b_home_work_distance = Beta('b_home_work_distance', 0, None, None, 0)
b_business_sector_agriculture = Beta('b_business_sector_agriculture', 0,
None, None, 0)
b_business_sector_production = Beta('b_business_sector_production', 0,
None, None, 0)
b_business_sector_wholesale = Beta('b_business_sector_wholesale', 0, None,
None, 1)
b_business_sector_retail = Beta('b_business_sector_retail', 0, None, None,
0)
b_business_sector_gastronomy = Beta('b_business_sector_gastronomy', 0,
None, None, 0)
b_business_sector_finance = Beta('b_business_sector_finance', 0, None,
None, 1)
b_business_sector_services_fc = Beta('b_business_sector_services_fc', 0,
None, None, 0)
b_business_sector_other_services = Beta('b_business_sector_other_services',
0, None, None, 1)
b_business_sector_others = Beta('b_business_sector_others', 0, None, None,
1)
b_business_sector_non_movers = Beta('b_business_sector_non_movers', 0,
None, None, 0)
b_employees = Beta('b_employees', 0, None, None, 1)
b_executives = Beta('b_executives', 0, None, None, 0)
b_german = Beta('b_german', 0, None, None, 0)
b_nationality_ch_germany_france_italy_nw_e = Beta(
'b_nationality_ch_germany_france_italy_nw_e', 0, None, None, 1)
b_nationality_south_west_europe = Beta('b_nationality_south_west_europe',
0, None, None, 1)
b_nationality_southeast_europe = Beta('b_nationality_southeast_europe', 0,
None, None, 1)
b_hh_income_na = Beta('B_hh_income_na', 0, None, None, 1)
b_hh_income_8000_or_less = Beta('b_hh_income_8000_or_less', 0, None, None,
0)
b_hh_income_more_than_8000 = Beta('b_hh_income_more_than_8000', 0, None,
None, 1)
# Definition of new variables
no_post_school_educ = ((highest_educ == 1) | (highest_educ == 2) |
(highest_educ == 3) | (highest_educ == 4))
secondary_education = ((highest_educ == 5) | (highest_educ == 6) |
(highest_educ == 7) | (highest_educ == 8) |
(highest_educ == 9) | (highest_educ == 10) |
(highest_educ == 11) | (highest_educ == 12))
tertiary_education = ((highest_educ == 13) | (highest_educ == 14) |
(highest_educ == 15) | (highest_educ == 16))
university = (highest_educ == 17)
male = (sex == 1)
public_transport_connection_quality_ARE_A_home = (
public_transport_connection_quality_ARE_home == 1)
public_transport_connection_quality_ARE_B_home = (
public_transport_connection_quality_ARE_home == 2)
public_transport_connection_quality_ARE_C_home = (
public_transport_connection_quality_ARE_home == 3)
public_transport_connection_quality_ARE_D_home = (
public_transport_connection_quality_ARE_home == 4)
public_transport_connection_quality_ARE_NA_home = (
public_transport_connection_quality_ARE_home == 5)
urban_work = (urban_typology_work == 1)
rural_work = (urban_typology_work == 3)
intermediate_work = (urban_typology_work == 2)
home_work_distance = (home_work_crow_fly_distance *
(home_work_crow_fly_distance >= 0.0) / 100000.0)
business_sector_agriculture = DefineVariable('business_sector_agriculture',
1 <= noga_08 <= 7, database)
business_sector_retail = DefineVariable('business_sector_retail',
47 <= noga_08 <= 47, database)
business_sector_gastronomy = DefineVariable('business_sector_gastronomy',
55 <= noga_08 <= 57, database)
business_sector_finance = DefineVariable('business_sector_finance',
64 <= noga_08 <= 67, database)
business_sector_production = DefineVariable(
'business_sector_production',
(10 <= noga_08 <= 35) | (40 <= noga_08 <= 44), database)
business_sector_wholesale = DefineVariable('business_sector_wholesale',
(45 <= noga_08 <= 45) |
(49 <= noga_08 <= 54), database)
business_sector_services_fC = DefineVariable(
'business_sector_services_fC',
(60 <= noga_08 <= 63) | (69 <= noga_08 <= 83) | (noga_08 == 58),
database)
business_sector_other_services = DefineVariable(
'business_sector_other_services', (86 <= noga_08 <= 90) |
(92 <= noga_08 <= 96) | (noga_08 == 59) | (noga_08 == 68), database)
business_sector_others = DefineVariable('business_sector_others',
97 <= noga_08 <= 98, database)
business_sector_non_movers = DefineVariable(
'business_sector_non_movers',
(8 <= noga_08 <= 9) | (36 <= noga_08 <= 39) | (84 <= noga_08 <= 85) |
(noga_08 == 91) | (noga_08 == 99), database)
employees = work_position == 2
executives = work_position == 1
german = language == 1
nationality_switzerland = nation == 8100
nationality_germany_austria_lichtenstein = (nation == 8207) + (
nation == 8229) + (nation == 8222)
nationality_italy_vatican = (nation == 8218) + (nation == 8241)
nationality_france_monaco_san_marino = (nation == 8212) + (
nation == 8226) + (nation == 8233)
nationality_northwestern_europe = (nation == 8204) + (nation == 8223) + (nation == 8227) + (nation == 8206) + \
(nation == 8211) + (nation == 8215) + (nation == 8216) + (nation == 8217) + \
(nation == 8228) + (nation == 8234)
nationality_south_west_europe = (nation == 8231) + (nation == 8236) + (
nation == 8202)
nationality_southeast_europe = (nation == 8224) + (nation == 8201) + (nation == 8214) + (nation == 8256) + \
(nation == 8250) + (nation == 8251) + (nation == 8252) + (nation == 8255) + \
(nation == 8205) + (nation == 8239) + (nation == 8242) + (nation == 8248) + \
(nation == 8254)
nationality_eastern_europe = (nation == 8230) + (nation == 8232) + (nation == 8240) + (nation == 8243) + \
(nation == 8244) + (nation == 8263) + (nation == 8265) + (nation == 8266) + \
(nation == 8260) + (nation == 8261) + (nation == 8262)
# several_part_time_jobs = full_part_time_job == 3
work_percentage = DefineVariable(
'work_percentage',
bioMin(
(full_part_time_job == 1) * 100 + percentage_first_part_time_job *
(percentage_first_part_time_job > 0), # +
# percentage_second_part_time_job * (percentage_second_part_time_job > 0),
100),
database)
hh_income_na = hh_income == -98
hh_income_less_than_2000 = hh_income == 1
hh_income_2000_to_4000 = hh_income == 2
hh_income_4001_to_6000 = hh_income == 3
hh_income_6001_to_8000 = hh_income == 4
hh_income_8001_to_10000 = hh_income == 5
hh_income_10001_to_12000 = hh_income == 6
hh_income_12001_to_14000 = hh_income == 7
hh_income_14001_to_16000 = hh_income == 8
hh_income_more_than_16000 = hh_income == 9
# Utility
U = alternative_specific_constant + \
b_executives * executives + \
b_employees * employees + \
b_no_post_school_education * no_post_school_educ + \
b_secondary_education * secondary_education + \
b_tertiary_education * tertiary_education + \
b_university * university + \
b_male * male + \
b_public_transport_connection_quality_are_a_home * public_transport_connection_quality_ARE_A_home + \
b_public_transport_connection_quality_are_b_home * public_transport_connection_quality_ARE_B_home + \
b_public_transport_connection_quality_are_c_home * public_transport_connection_quality_ARE_C_home + \
b_public_transport_connection_quality_are_d_home * public_transport_connection_quality_ARE_D_home + \
b_public_transport_connection_quality_are_na_home * public_transport_connection_quality_ARE_NA_home + \
b_urban_work * urban_work + \
b_rural_work * rural_work + \
b_intermediate_work * intermediate_work + \
b_home_work_distance * home_work_distance + \
models.piecewiseFormula(age, [0, 20, 35, 75, 200]) + \
b_business_sector_agriculture * business_sector_agriculture + \
b_business_sector_retail * business_sector_retail + \
b_business_sector_gastronomy * business_sector_gastronomy + \
b_business_sector_finance * business_sector_finance + \
b_business_sector_production * business_sector_production + \
b_business_sector_wholesale * business_sector_wholesale + \
b_business_sector_services_fc * business_sector_services_fC + \
b_business_sector_other_services * business_sector_other_services + \
b_business_sector_others * business_sector_others + \
b_business_sector_non_movers * business_sector_non_movers + \
b_german * german + \
b_nationality_ch_germany_france_italy_nw_e * nationality_switzerland + \
b_nationality_ch_germany_france_italy_nw_e * nationality_germany_austria_lichtenstein + \
b_nationality_ch_germany_france_italy_nw_e * nationality_italy_vatican + \
b_nationality_ch_germany_france_italy_nw_e * nationality_france_monaco_san_marino + \
b_nationality_ch_germany_france_italy_nw_e * nationality_northwestern_europe + \
b_nationality_south_west_europe * nationality_south_west_europe + \
b_nationality_southeast_europe * nationality_southeast_europe + \
b_nationality_ch_germany_france_italy_nw_e * nationality_eastern_europe + \
models.piecewiseFormula(work_percentage, [0, 90, 101]) + \
b_hh_income_na * hh_income_na + \
b_hh_income_8000_or_less * hh_income_less_than_2000 + \
b_hh_income_8000_or_less * hh_income_2000_to_4000 + \
b_hh_income_8000_or_less * hh_income_4001_to_6000 + \
b_hh_income_8000_or_less * hh_income_6001_to_8000 + \
b_hh_income_more_than_8000 * hh_income_8001_to_10000 + \
b_hh_income_more_than_8000 * hh_income_10001_to_12000 + \
b_hh_income_more_than_8000 * hh_income_12001_to_14000 + \
b_hh_income_more_than_8000 * hh_income_14001_to_16000 + \
b_hh_income_more_than_8000 * hh_income_more_than_16000
U_No_telecommuting = 0
# Associate utility functions with the numbering of alternatives
V = {
1: U, # Yes or sometimes
0: U_No_telecommuting
} # No
av = {1: 1, 0: 1}
# The choice model is a logit, with availability conditions
prob_telecommuting = models.logit(V, av, 1)
prob_no_telecommuting = models.logit(V, av, 0)
simulate = {
'Prob. telecommuting': prob_telecommuting,
'Prob. no telecommuting': prob_no_telecommuting
}
# Create the Biogeme object
biogeme = bio.BIOGEME(database, simulate)
biogeme.modelName = 'logit_telecommuting_simul'
# Change the working directory, so that biogeme writes in the correct folder, i.e., where this file is
# standard_directory = os.getcwd()
# os.chdir(output_directory_for_simulation)
results = biogeme.simulate(theBetaValues=betas)
# print(results.describe())
df_persons = pd.concat([df_persons, results], axis=1)
# Go back to the normal working directory
# os.chdir(standard_directory)
''' Save the file '''
df_persons.to_csv(output_directory_for_simulation / output_file_name,
sep=',',
index=False)
def run_estimation(data_file_directory,
data_file_name,
output_directory,
output_file_name='logit_telecommuting'):
"""
:author: Antonin Danalet, based on the example '01logit.py' by Michel Bierlaire, EPFL, on biogeme.epfl.ch
A binary logit model on the possibility to work from home at least some times."""
# Read the data
df = pd.read_csv(data_file_directory / data_file_name, ';')
database = db.Database('persons', df)
# The following statement allows you to use the names of the variable as Python variable.
globals().update(database.variables)
# Parameters to be estimated
alternative_specific_constant = Beta('alternative_specific_constant', 0,
None, None, 0)
b_no_post_school_education = Beta('b_no_post_school_education', 0, None,
None, 0)
b_secondary_education = Beta('b_secondary_education', 0, None, None, 0)
b_tertiary_education = Beta('b_tertiary_education', 0, None, None, 0)
b_university = Beta('b_university', 0, None, None, 1)
b_male = Beta('b_male', 0, None, None, 1)
b_single_household = Beta('b_single_household', 0, None, None, 1)
b_couple_without_children = Beta('b_couple_without_children', 0, None,
None, 0)
b_couple_with_children = Beta('b_couple_with_children', 0, None, None, 1)
b_single_parent_with_children = Beta('b_single_parent_with_children', 0,
None, None, 1)
b_not_family_household = Beta('b_not_family_household', 0, None, None, 1)
b_public_transport_connection_quality_are_a_home = Beta(
'b_public_transport_connection_quality_are_a_home', 0, None, None, 1)
b_public_transport_connection_quality_are_b_home = Beta(
'b_public_transport_connection_quality_are_b_home', 0, None, None, 1)
b_public_transport_connection_quality_are_c_home = Beta(
'b_public_transport_connection_quality_are_c_home', 0, None, None, 1)
b_public_transport_connection_quality_are_d_home = Beta(
'b_public_transport_connection_quality_are_d_home', 0, None, None, 1)
b_public_transport_connection_quality_are_na_home = Beta(
'b_public_transport_connection_quality_are_na_home', 0, None, None, 0)
b_public_transport_connection_quality_are_a_work = Beta(
'b_public_transport_connection_quality_are_a_work', 0, None, None, 1)
b_public_transport_connection_quality_are_b_work = Beta(
'b_public_transport_connection_quality_are_b_work', 0, None, None, 1)
b_public_transport_connection_quality_are_c_work = Beta(
'b_public_transport_connection_quality_are_c_work', 0, None, None, 1)
b_public_transport_connection_quality_are_d_work = Beta(
'b_public_transport_connection_quality_are_d_work', 0, None, None, 1)
b_public_transport_connection_quality_are_na_work = Beta(
'b_public_transport_connection_quality_are_na_work', 0, None, None, 1)
b_urban_home = Beta('b_urban_home', 0, None, None, 1)
b_rural_home = Beta('b_rural_home', 0, None, None, 1)
b_intermediate_home = Beta('b_intermediate_home', 0, None, None, 1)
b_urban_work = Beta('b_urban_work', 0, None, None, 1)
b_rural_work = Beta('b_rural_work', 0, None, None, 1)
b_intermediate_work = Beta('b_intermediate_work', 0, None, None, 1)
b_home_work_distance = Beta('b_home_work_distance', 0, None, None, 0)
b_home_work_distance_zero = Beta('b_home_work_distance_zero', 0, None,
None, 0)
b_home_work_distance_na = Beta('b_home_work_distance_na', 0, None, None, 0)
b_business_sector_agriculture = Beta('b_business_sector_agriculture', 0,
None, None, 1)
b_business_sector_production = Beta('b_business_sector_production', 0,
None, None, 0)
b_business_sector_wholesale = Beta('b_business_sector_wholesale', 0, None,
None, 0)
b_business_sector_retail = Beta('b_business_sector_retail', 0, None, None,
0)
b_business_sector_gastronomy = Beta('b_business_sector_gastronomy', 0,
None, None, 0)
b_business_sector_finance = Beta('b_business_sector_finance', 0, None,
None, 0)
b_business_sector_services_fc = Beta('b_business_sector_services_fc', 0,
None, None, 1)
b_business_sector_other_services = Beta('b_business_sector_other_services',
0, None, None, 0)
b_business_sector_others = Beta('b_business_sector_others', 0, None, None,
0)
b_business_sector_non_movers = Beta('b_business_sector_non_movers', 0,
None, None, 0)
b_employees = Beta('b_employees', 0, None, None, 1)
b_executives = Beta('b_executives', 0, None, None, 0)
b_german = Beta('b_german', 0, None, None, 0)
b_nationality_ch_germany_france_italy_nw_e = Beta(
'b_nationality_ch_germany_france_italy_nw_e', 0, None, None, 1)
b_nationality_south_west_europe = Beta('b_nationality_south_west_europe',
0, None, None, 1)
b_nationality_southeast_europe = Beta('b_nationality_southeast_europe', 0,
None, None, 1)
# b_several_part_time_jobs = Beta('b_several_part_time_jobs', 0, None, None, 1)
b_hh_income_na = Beta('b_hh_income_na', 0, None, None, 0)
b_hh_income_8000_or_less = Beta('b_hh_income_8000_or_less', 0, None, None,
0)
b_hh_income_more_than_8000 = Beta('b_hh_income_more_than_8000', 0, None,
None, 1)
b_general_abo = Beta('b_general_abo', 0, None, None, 0)
b_regional_abo = Beta('b_regional_abo', 0, None, None, 1)
b_regional_abo_na = Beta('b_regional_abo_na', 0, None, None, 1)
b_half_fare_abo = Beta('b_half_fare_abo', 0, None, None, 1)
b_half_fare_abo_na = Beta('b_half_fare_abo_na', 0, None, None, 1)
b_car_avail = Beta('b_car_avail', 0, None, None, 1)
b_car_avail_na = Beta('b_car_avail_na', 0, None, None, 1)
b_mobility_resource_na = Beta('b_mobility_resource_na', 0, None, None, 0)
b_mobility_resource_car_general_abo = Beta(
'b_mobility_resource_car_general_abo', 0, None, None, 1)
b_mobility_resource_car_half_fare_abo = Beta(
'b_mobility_resource_car_half_fare_abo', 0, None, None, 0)
b_mobility_resource_car = Beta('b_mobility_resource_car', 0, None, None, 1)
b_mobility_resource_general_abo = Beta('b_mobility_resource_general_abo',
0, None, None, 1)
b_mobility_resource_half_fare_abo = Beta(
'b_mobility_resource_half_fare_abo', 0, None, None, 1)
b_mobility_resource_none = Beta('b_mobility_resource_none', 0, None, None,
1)
b_mobility_resource_car_half_fare_regional_abo = Beta(
'b_mobility_resource_car_half_fare_regional_abo', 0, None, None, 1)
b_mobility_resource_car_regional_abo = Beta(
'b_mobility_resource_car_regional_abo', 0, None, None, 1)
b_mobility_resource_half_fare_regional_abo = Beta(
'b_mobility_resource_half_fare_regional_abo', 0, None, None, 1)
b_mobility_resource_regional_abo = Beta('b_mobility_resource_regional_abo',
0, None, None, 1)
# Definition of new variables
male = DefineVariable('male', sex == 1, database)
single_household = DefineVariable('single_household', hh_type == 10,
database)
couple_without_children = DefineVariable('couple_without_children',
hh_type == 210, database)
couple_with_children = DefineVariable('couple_with_children',
hh_type == 220, database)
single_parent_with_children = DefineVariable('single_parent_with_children',
hh_type == 230, database)
not_family_household = DefineVariable('not_family_household',
hh_type == 30, database)
public_transport_connection_quality_ARE_A_home = DefineVariable(
'public_transport_connection_quality_ARE_A_home',
public_transport_connection_quality_ARE_home == 1, database)
public_transport_connection_quality_ARE_B_home = DefineVariable(
'public_transport_connection_quality_ARE_B_home',
public_transport_connection_quality_ARE_home == 2, database)
public_transport_connection_quality_ARE_C_home = DefineVariable(
'public_transport_connection_quality_ARE_C_home',
public_transport_connection_quality_ARE_home == 3, database)
public_transport_connection_quality_ARE_D_home = DefineVariable(
'public_transport_connection_quality_ARE_D_home',
public_transport_connection_quality_ARE_home == 4, database)
public_transport_connection_quality_ARE_NA_home = DefineVariable(
'public_transport_connection_quality_ARE_NA_home',
public_transport_connection_quality_ARE_home == 5, database)
public_transport_connection_quality_ARE_A_work = DefineVariable(
'public_transport_connection_quality_ARE_A_work',
public_transport_connection_quality_ARE_work == 1, database)
public_transport_connection_quality_ARE_B_work = DefineVariable(
'public_transport_connection_quality_ARE_B_work',
public_transport_connection_quality_ARE_work == 2, database)
public_transport_connection_quality_ARE_C_work = DefineVariable(
'public_transport_connection_quality_ARE_C_work',
public_transport_connection_quality_ARE_work == 3, database)
public_transport_connection_quality_ARE_D_work = DefineVariable(
'public_transport_connection_quality_ARE_D_work',
public_transport_connection_quality_ARE_work == 4, database)
public_transport_connection_quality_ARE_NA_work = DefineVariable(
'public_transport_connection_quality_ARE_NA_work',
public_transport_connection_quality_ARE_work == 5, database)
urban_home = DefineVariable('urban_home', urban_typology_home == 1,
database)
rural_home = DefineVariable('rural_home', urban_typology_home == 3,
database)
intermediate_home = DefineVariable('intermediate_home',
urban_typology_home == 2, database)
urban_work = DefineVariable('urban_work', urban_typology_work == 1,
database)
rural_work = DefineVariable('rural_work', urban_typology_work == 3,
database)
intermediate_work = DefineVariable('intermediate_work',
urban_typology_work == 2, database)
home_work_distance = DefineVariable(
'home_work_distance',
home_work_crow_fly_distance * (home_work_crow_fly_distance >= 0.0) /
100000.0, database)
home_work_distance_zero = DefineVariable(
'home_work_distance_zero', home_work_crow_fly_distance == 0.0,
database)
home_work_distance_na = DefineVariable('home_work_distance_na',
home_work_crow_fly_distance == -999,
database)
employees = DefineVariable('employees', work_position == 2, database)
executives = DefineVariable('executives', work_position == 1, database)
german = DefineVariable('german', language == 1, database)
nationality_switzerland = DefineVariable('nationality_switzerland',
nation == 8100, database)
nationality_germany_austria_lichtenstein = DefineVariable(
'nationality_germany_austria_lichtenstein',
(nation == 8207) | (nation == 8229) | (nation == 8222), database)
nationality_italy_vatican = DefineVariable(
'nationality_italy_vatican', (nation == 8218) | (nation == 8241),
database)
nationality_france_monaco_san_marino = DefineVariable(
'nationality_france_monaco_san_marino',
(nation == 8212) | (nation == 8226) | (nation == 8233), database)
nationality_northwestern_europe = DefineVariable(
'nationality_northwestern_europe',
(nation == 8204) | # Belgium
(nation == 8223) | # Luxembourg
(nation == 8227) | # Netherlands
(nation == 8206) | # Denmark
(nation == 8211) | # Finland
(nation == 8215) | # United Kingdom
(nation == 8216) | # Ireland
(nation == 8217) | # Iceland
(nation == 8228) | # Norway
(nation == 8234), # Sweden
database)
nationality_south_west_europe = DefineVariable(
'nationality_south_west_europe',
(nation == 8231) | # Portugal
(nation == 8236) | # Spain
(nation == 8202), # Andorra
database)
nationality_southeast_europe = DefineVariable(
'nationality_southeast_europe',
(nation == 8224) | # Malta
(nation == 8201) | # Albania
(nation == 8214) | # Greece
(nation == 8256) | # Kosovo
(nation == 8250) | # Croatia
(nation == 8251) | # Slovenia
(nation == 8252) | # Bosnia and Herzegovina
(nation == 8255) | # Macedonia
(nation == 8205) | # Bulgaria
(nation == 8239) | # Turkey
(nation == 8242) | # Cyprus
(nation == 8248) | # Serbia
(nation == 8254), # Montenegro
database)
nationality_eastern_europe = DefineVariable(
'nationality_eastern_europe',
(nation == 8230) | # Poland
(nation == 8232) | # Rumania
(nation == 8240) | # Hungary
(nation == 8243) | # Slovakia
(nation == 8244) | # Czech Republic
(nation == 8263) | # Moldavia
(nation == 8265) | # Ukraine
(nation == 8266) | # Belarus
(nation == 8260) | # Estonia
(nation == 8261) | # Latvia
(nation == 8262), # Lithuania
database)
# several_part_time_jobs = DefineVariable('several_part_time_jobs', full_part_time_job == 3, database)
hh_income_na = DefineVariable('hh_income_na', hh_income < 0, database)
hh_income_less_than_2000 = DefineVariable('hh_income_less_than_4000',
hh_income == 1, database)
hh_income_2000_to_4000 = DefineVariable('hh_income_2000_to_4000',
hh_income == 2, database)
hh_income_4001_to_6000 = DefineVariable('hh_income_4001_to_6000',
hh_income == 3, database)
hh_income_6001_to_8000 = DefineVariable('hh_income_6001_to_8000',
hh_income == 4, database)
hh_income_8001_to_10000 = DefineVariable('hh_income_8001_to_10000',
hh_income == 5, database)
hh_income_10001_to_12000 = DefineVariable('hh_income_10001_to_12000',
hh_income == 6, database)
hh_income_12001_to_14000 = DefineVariable('hh_income_12001_to_14000',
hh_income == 7, database)
hh_income_14001_to_16000 = DefineVariable('hh_income_14001_to_16000',
hh_income == 8, database)
hh_income_more_than_16000 = DefineVariable('hh_income_more_than_16000',
hh_income == 9, database)
general_abo = DefineVariable('general_abo', GA_ticket == 1, database)
regional_abo = DefineVariable('regional_abo', Verbund_Abo == 1, database)
half_fare_abo = DefineVariable('half_fare_abo', halbtax_ticket == 1,
database)
car_avail_always_or_on_demand = DefineVariable(
'car_avail_always_or_on_demand', (car_avail == 1) | (car_avail == 2),
database)
regional_abo_na = DefineVariable('regional_abo_na', Verbund_Abo < 0,
database)
half_fare_abo_na = DefineVariable('half_fare_abo_na', halbtax_ticket < 0,
database)
car_avail_na = DefineVariable('car_avail_na', car_avail < 0, database)
mobility_resource_na = DefineVariable('mobility_resource_na',
mobility_resources == -98, database)
mobility_resource_car_general_abo = DefineVariable(
'mobility_resource_car_general_abo', mobility_resources == 1, database)
mobility_resource_car_half_fare_abo = DefineVariable(
'mobility_resource_car_half_fare_abo', mobility_resources == 2,
database)
mobility_resource_car = DefineVariable('mobility_resource_car',
mobility_resources == 3, database)
mobility_resource_general_abo = DefineVariable(
'mobility_resource_general_abo', mobility_resources == 4, database)
mobility_resource_half_fare_abo = DefineVariable(
'mobility_resource_half_fare_abo', mobility_resources == 5, database)
mobility_resource_none = DefineVariable('mobility_resource_none',
mobility_resources == 6, database)
mobility_resource_car_half_fare_regional_abo = DefineVariable(
'mobility_resource_car_half_fare_regional_abo',
mobility_resources == 20, database)
mobility_resource_car_regional_abo = DefineVariable(
'mobility_resource_car_regional_abo', mobility_resources == 30,
database)
mobility_resource_half_fare_regional_abo = DefineVariable(
'mobility_resource_half_fare_regional_abo', mobility_resources == 50,
database)
mobility_resource_regional_abo = DefineVariable(
'mobility_resource_regional_abo', mobility_resources == 60, database)
# Utility
U = alternative_specific_constant + \
b_executives * executives + \
b_employees * employees + \
b_no_post_school_education * no_post_school_educ + \
b_secondary_education * secondary_education + \
b_tertiary_education * tertiary_education + \
b_university * university + \
b_male * male + \
b_single_household * single_household + \
b_couple_without_children * couple_without_children + \
b_couple_with_children * couple_with_children + \
b_single_parent_with_children * single_parent_with_children + \
b_not_family_household * not_family_household + \
b_public_transport_connection_quality_are_a_home * public_transport_connection_quality_ARE_A_home + \
b_public_transport_connection_quality_are_b_home * public_transport_connection_quality_ARE_B_home + \
b_public_transport_connection_quality_are_c_home * public_transport_connection_quality_ARE_C_home + \
b_public_transport_connection_quality_are_d_home * public_transport_connection_quality_ARE_D_home + \
b_public_transport_connection_quality_are_na_home * public_transport_connection_quality_ARE_NA_home + \
b_public_transport_connection_quality_are_a_work * public_transport_connection_quality_ARE_A_work + \
b_public_transport_connection_quality_are_b_work * public_transport_connection_quality_ARE_B_work + \
b_public_transport_connection_quality_are_c_work * public_transport_connection_quality_ARE_C_work + \
b_public_transport_connection_quality_are_d_work * public_transport_connection_quality_ARE_D_work + \
b_public_transport_connection_quality_are_na_work * public_transport_connection_quality_ARE_NA_work + \
b_urban_home * urban_home + \
b_rural_home * rural_home + \
b_intermediate_home * intermediate_home + \
b_urban_work * urban_work + \
b_rural_work * rural_work + \
b_intermediate_work * intermediate_work + \
b_home_work_distance * home_work_distance + \
b_home_work_distance_zero * home_work_distance_zero + \
b_home_work_distance_na * home_work_distance_na + \
models.piecewiseFormula(age, [15, 19, 31, 79, 85]) + \
b_business_sector_agriculture * business_sector_agriculture + \
b_business_sector_retail * business_sector_retail + \
b_business_sector_gastronomy * business_sector_gastronomy + \
b_business_sector_finance * business_sector_finance + \
b_business_sector_production * business_sector_production + \
b_business_sector_wholesale * business_sector_wholesale + \
b_business_sector_services_fc * business_sector_services_fc + \
b_business_sector_other_services * business_sector_other_services + \
b_business_sector_others * business_sector_others + \
b_business_sector_non_movers * business_sector_non_movers + \
b_german * german + \
b_nationality_ch_germany_france_italy_nw_e * nationality_switzerland + \
b_nationality_ch_germany_france_italy_nw_e * nationality_germany_austria_lichtenstein + \
b_nationality_ch_germany_france_italy_nw_e * nationality_italy_vatican + \
b_nationality_ch_germany_france_italy_nw_e * nationality_france_monaco_san_marino + \
b_nationality_ch_germany_france_italy_nw_e * nationality_northwestern_europe + \
b_nationality_south_west_europe * nationality_south_west_europe + \
b_nationality_southeast_europe * nationality_southeast_europe + \
b_nationality_ch_germany_france_italy_nw_e * nationality_eastern_europe + \
models.piecewiseFormula(work_percentage, [0, 90, 101]) + \
b_hh_income_na * hh_income_na + \
b_hh_income_8000_or_less * hh_income_less_than_2000 + \
b_hh_income_8000_or_less * hh_income_2000_to_4000 + \
b_hh_income_8000_or_less * hh_income_4001_to_6000 + \
b_hh_income_8000_or_less * hh_income_6001_to_8000 + \
b_hh_income_more_than_8000 * hh_income_8001_to_10000 + \
b_hh_income_more_than_8000 * hh_income_10001_to_12000 + \
b_hh_income_more_than_8000 * hh_income_12001_to_14000 + \
b_hh_income_more_than_8000 * hh_income_14001_to_16000 + \
b_hh_income_more_than_8000 * hh_income_more_than_16000 + \
b_general_abo * general_abo + \
b_regional_abo * regional_abo + \
b_half_fare_abo * half_fare_abo + \
b_car_avail * car_avail_always_or_on_demand + \
b_regional_abo_na * regional_abo_na + \
b_half_fare_abo_na * half_fare_abo_na + \
b_car_avail_na * car_avail_na + \
b_mobility_resource_na * mobility_resource_na + \
b_mobility_resource_car_general_abo * mobility_resource_car_general_abo + \
b_mobility_resource_car_half_fare_abo * mobility_resource_car_half_fare_abo + \
b_mobility_resource_car * mobility_resource_car + \
b_mobility_resource_general_abo * mobility_resource_general_abo + \
b_mobility_resource_half_fare_abo * mobility_resource_half_fare_abo + \
b_mobility_resource_none * mobility_resource_none + \
b_mobility_resource_car_half_fare_regional_abo * mobility_resource_car_half_fare_regional_abo + \
b_mobility_resource_car_regional_abo * mobility_resource_car_regional_abo + \
b_mobility_resource_half_fare_regional_abo * mobility_resource_half_fare_regional_abo + \
b_mobility_resource_regional_abo * mobility_resource_regional_abo
U_no_telecommuting = 0
# Associate utility functions with the numbering of alternatives
V = {
1: U, # Yes or sometimes
0: U_no_telecommuting
} # No
av = {1: 1, 0: 1}
# Definition of the model. This is the contribution of each
# observation to the log likelihood function.
logprob = models.loglogit(
V,
av, # All alternatives are supposed to be always available
telecommuting) # Choice variable
# Change the working directory, so that biogeme writes in the correct folder
standard_directory = os.getcwd()
os.chdir(output_directory)
# Create the Biogeme object
biogeme = bio.BIOGEME(database, logprob)
biogeme.modelName = output_file_name
# Estimate the parameters
results = biogeme.estimate()
# Get the results in LaTeX
results.writeLaTeX()
# Go back to the normal working directory
os.chdir(standard_directory)
