# Read the estimates from the previous estimation, and use
# them as starting values
try:
results = res.bioResults(pickleFile='05latentChoiceFull.pickle')
except FileNotFoundError:
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)
database.remove(exclude)
# Parameters to be estimated
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 1)
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
LAMBDA = Beta('LAMBDA', 1.5, 0.0001, 5, 0)
# Definition of new variables
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
# Definition of new variables: adding columns to the database
CAR_AV_SP = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), database)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0), database)
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED', TRAIN_TT / 100.0, database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED', TRAIN_COST / 100,
database)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0, database)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / 100, database)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100, database)
CAR_CO_SCALED = DefineVariable('CAR_CO_SCALED', CAR_CO / 100, database)
# Definition of the utility functions
V1 = ASC_TRAIN + \
B_TIME * models.boxcox(TRAIN_TT_SCALED, LAMBDA) + \
B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + \
B_TIME * models.boxcox(SM_TT_SCALED, LAMBDA) + \
#database.data.drop(database.data[remove].index,inplace=True)
# Here we use the "biogeme" way for backward compatibility
exclude = ((PURPOSE != 1) * (PURPOSE != 3) + (CHOICE == 0)) > 0
database.remove(exclude)
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 1)
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
TRAIN_TT / 100.0,database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
TRAIN_COST / 100,database)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0, database)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / 100, database)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100, database)
CAR_CO_SCALED = DefineVariable('CAR_CO_SCALED', CAR_CO / 100, database)
# Biogeme cannot compute the log of 0. Therefore, whenever the cost
# is 0, the log of 1 computed instead.
LOG_CAR_COST = DefineVariable('LOG_CAR_COST', (CAR_CO_SCALED != 0) *
log(CAR_CO_SCALED + 1 * (CAR_CO_SCALED == 0)),
database)
LOG_TRAIN_COST = DefineVariable('LOG_TRAIN_COST', (TRAIN_COST_SCALED != 0) *
log(TRAIN_COST_SCALED + 1 *
(TRAIN_COST_SCALED == 0)), database)
# 4 Upper bound
# 5 0: estimate the parameter, 1: keep it fixed
Constant1 = Beta('Constant1', 0, None, None, 1)
Constant2 = Beta('Constant2', 0, None, None, 0)
Constant3 = Beta('Constant3', 0, None, None, 0)
LogFare = Beta('LogFare', 0, None, None, 0)
Legroom = Beta('Legroom', 0, None, None, 0)
SchedDE = Beta('SchedDE', 0, None, None, 0)
SchedDL = Beta('SchedDL', 0, None, None, 0)
Total_TT1 = Beta('Total_TT1', 0, None, None, 0)
Total_TT2 = Beta('Total_TT2', 0, None, None, 0)
Total_TT3 = Beta('Total_TT3', 0, None, None, 0)
# Define here arithmetic expressions for variables that are not directly available from the data
DepTimeSensitive = DefineVariable('DepTimeSensitive',
q11_DepartureOrArrivalIsImportant == 1,
database)
ArrTimeSensitive = DefineVariable('ArrTimeSensitive',
q11_DepartureOrArrivalIsImportant == 2,
database)
DesiredDepTime = DefineVariable('DesiredDepTime', q12_IdealDepTime, database)
DesiredArrTime = DefineVariable('DesiredArrTime', q13_IdealArrTime, database)
SchedDelay_1 = DefineVariable(
'SchedDelay_1',
(DepTimeSensitive * (DepartureTimeMins_1 - DesiredDepTime)) +
(ArrTimeSensitive * (ArrivalTimeMins_1 - DesiredArrTime)), database)
SchedDelay_2 = DefineVariable(
'SchedDelay_2',
(DepTimeSensitive * (DepartureTimeMins_2 - DesiredDepTime)) +
(ArrTimeSensitive * (ArrivalTimeMins_2 - DesiredArrTime)), database)
SchedDelay_3 = DefineVariable(
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 1)
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
# Definition of new variables
SM_COST = [Variable(f'SM_CO_{q}') * (GA == 0) for q in range(nbrQuestions)]
TRAIN_COST = [
Variable(f'TRAIN_CO_{q}') * (GA == 0) for q in range(nbrQuestions)
]
# Definition of new variables: adding columns to the database
CAR_AV_SP = [
DefineVariable(f'CAR_AV_SP_{q}',
Variable(f'CAR_AV_{q}') * (SP != 0), database)
for q in range(nbrQuestions)
]
TRAIN_AV_SP = [
DefineVariable(f'TRAIN_AV_SP_{q}',
Variable(f'TRAIN_AV_{q}') * (SP != 0), database)
for q in range(nbrQuestions)
]
SM_AV = [Variable(f'SM_AV_{q}') for q in range(nbrQuestions)]
TRAIN_TT_SCALED = [
DefineVariable(f'TRAIN_TT_SCALED_{q}',
Variable(f'TRAIN_TT_{q}') / 100.0, database)
for q in range(nbrQuestions)
import biogeme.models as models
from biogeme.expressions import Beta, DefineVariable, Elem, bioNormalCdf
# Read the data
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)
database.remove(exclude)
# Parameters to be estimated
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
# Parameters for the ordered logit.
# tau1 <= 0
tau1 = Beta('tau1', -1, None, 0, 0)
# delta2 >= 0
delta2 = Beta('delta2', 2, 0, None, 0)
tau2 = tau1 + delta2
# Definition of new variables
TRAIN_COST = TRAIN_CO * (GA == 0)
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED', TRAIN_TT / 100.0, database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED', TRAIN_COST / 100,
database)
# Utility
U = B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
# Associate each discrete indicator with an interval.
# 1: -infinity -> tau1
# 2: tau1 -> tau2
# 3: tau2 -> +infinity
ChoiceProba = {
1: 1 - dist.logisticcdf(U - tau1),
2: dist.logisticcdf(U - tau1) - dist.logisticcdf(U - tau2),
3: dist.logisticcdf(U - tau2)
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)
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)
##Method 1 from python
"""
TimePT_scaled = TimePT / 200
TimeCar_scaled = TimeCar / 200
MarginalCostPT_scaled = MarginalCostPT / 10
CostCarCHF_scaled = CostCarCHF / 10
distance_km_scaled = distance_km / 5
male = (Gender == 1)
female = (Gender == 2)
unreportedGender = (Gender == -1)
fulltime = (OccupStat == 1)
notfulltime = (OccupStat != 1)
"""
##Method 2 by adding columns
TimePT_scaled = DefineVariable('TimePT_scaled', TimePT / 200, database)
TimeCar_scaled = DefineVariable('TimeCar_scaled', TimeCar / 200, database)
MarginalCostPT_scaled = DefineVariable('MarginalCostPT_scaled',
MarginalCostPT / 10, database)
CostCarCHF_scaled = DefineVariable('CostCarCHF_scaled', CostCarCHF / 10,
database)
distance_km_scaled = DefineVariable('distance_km_scaled', distance_km / 5,
database)
male = DefineVariable('male', Gender == 1, database)
female = DefineVariable('female', Gender == 2, database)
unreportedGender = DefineVariable('unreportedGender', Gender == -1, database)
fulltime = DefineVariable('fulltime', OccupStat == 1, database)
notfulltime = DefineVariable('notfulltime', OccupStat != 1, database)
def cv_test_model(V,
R,
Draws,
ModName,
test,
myRandomNumberGenerators,
COST_SCALE_CAR=100,
COST_SCALE_PUB=100):
db_test = db.Database("swissmetro_test", test)
db_test.setRandomNumberGenerators(myRandomNumberGenerators)
globals().update(db_test.variables)
#locals().update(db_test.variables)
#define variables
#variables
CAR_AV_SP = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), db_test)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0), db_test)
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
###Parameters to be estimated (Note not all parameters are used in all models!)
##Attributes
#Alternative specific constants
ASC_CAR = Beta('ASC_CAR', 0, None, None, 1)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 0)
#Cost (Note: Assumed generic)
B_COST = Beta('B_COST', 0, None, None, 0)
B_COST_BUSINESS = Beta('B_COST_BUSINESS', 0, None, None, 0)
B_COST_PRIVATE = Beta('B_COST_PRIVATE', 0, None, None, 0)
#Time
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_TIME_CAR = Beta('B_TIME_CAR', 0, None, None, 0)
B_TIME_TRAIN = Beta('B_TIME_TRAIN', 0, None, None, 0)
B_TIME_SM = Beta('B_TIME_SM', 0, None, None, 0)
B_TIME_PUB = Beta('B_TIME_PUB', 0, None, None, 0)
B_TIME_CAR_BUSINESS = Beta('B_TIME_CAR_BUSINESS', 0, None, None, 0)
B_TIME_TRAIN_BUSINESS = Beta('B_TIME_TRAIN_BUSINESS', 0, None, None, 0)
B_TIME_SM_BUSINESS = Beta('B_TIME_SM_BUSINESS', 0, None, None, 0)
B_TIME_PUB_BUSINESS = Beta('B_TIME_PUB_BUSINESS', 0, None, None, 0)
B_TIME_CAR_PRIVATE = Beta('B_TIME_CAR_PRIVATE', 0, None, None, 0)
B_TIME_TRAIN_PRIVATE = Beta('B_TIME_TRAIN_PRIVATE', 0, None, None, 0)
B_TIME_SM_PRIVATE = Beta('B_TIME_SM_PRIVATE', 0, None, None, 0)
B_TIME_PUB_PRIVATE = Beta('B_TIME_PUB_PRIVATE', 0, None, None, 0)
#HE (Note: Not available for car)
B_HE = Beta('B_HE', 0, None, None, 0)
B_HE_TRAIN = Beta('B_HE_TRAIN', 0, None, None, 0)
B_HE_SM = Beta('B_HE_SM', 0, None, None, 0)
B_HE_BUSINESS = Beta('B_HE_BUSINESS', 0, None, None, 0)
B_HE_TRAIN_BUSINESS = Beta('B_HE_TRAIN_BUSINESS', 0, None, None, 0)
B_HE_SM_BUSINESS = Beta('B_HE_SM_BUSINESS', 0, None, None, 0)
B_HE_PRIVATE = Beta('B_HE_PRIVATE', 0, None, None, 0)
B_HE_TRAIN_PRIVATE = Beta('B_HE_TRAIN_PRIVATE', 0, None, None, 0)
B_HE_SM_PRIVATE = Beta('B_HE_SM_PRIVATE', 0, None, None, 0)
#Seats (Note: Only avaliable for SM)
B_SEATS = Beta('B_SEATS', 0, None, None, 0)
##Characteristics
#Age
B_AGE_1_TRAIN = Beta('B_AGE_1_TRAIN', 0, None, None, 1) #Note: Reference
B_AGE_2_TRAIN = Beta('B_AGE_2_TRAIN', 0, None, None, 0)
B_AGE_3_TRAIN = Beta('B_AGE_3_TRAIN', 0, None, None, 0)
B_AGE_4_TRAIN = Beta('B_AGE_4_TRAIN', 0, None, None, 0)
B_AGE_5_TRAIN = Beta('B_AGE_5_TRAIN', 0, None, None, 0)
B_AGE_6_TRAIN = Beta('B_AGE_6_TRAIN', 0, None, None, 0)
B_AGE_1_SM = Beta('B_AGE_1_SM', 0, None, None, 1) #Note: Reference
B_AGE_2_SM = Beta('B_AGE_2_SM', 0, None, None, 0)
B_AGE_3_SM = Beta('B_AGE_3_SM', 0, None, None, 0)
B_AGE_4_SM = Beta('B_AGE_4_SM', 0, None, None, 0)
B_AGE_5_SM = Beta('B_AGE_5_SM', 0, None, None, 0)
B_AGE_6_SM = Beta('B_AGE_6_SM', 0, None, None, 0)
B_AGE_1_PUB = Beta('B_AGE_1_PUB', 0, None, None, 1) #Note: Reference
B_AGE_2_PUB = Beta('B_AGE_2_PUB', 0, None, None, 0)
B_AGE_3_PUB = Beta('B_AGE_3_PUB', 0, None, None, 0)
B_AGE_4_PUB = Beta('B_AGE_4_PUB', 0, None, None, 0)
B_AGE_5_PUB = Beta('B_AGE_5_PUB', 0, None, None, 0)
B_AGE_6_PUB = Beta('B_AGE_6_PUB', 0, None, None, 0)
B_AGE_ADULTS_TRAIN = Beta('B_AGE_TRAIN_ADULTS', 0, None, None, 0)
B_AGE_ADULTS_SM = Beta('B_AGE_ADULTS_SM', 0, None, None, 0)
B_AGE_ADULTS_PUB = Beta('B_AGE_ADULTS_PUB', 0, None, None, 0)
#Luggage
B_LUGGAGE_TRAIN = Beta('B_LUGGAGE_TRAIN', 0, None, None, 0)
B_LUGGAGE_SM = Beta('B_LUGGAGE_SM', 0, None, None, 0)
B_LUGGAGE_PUB = Beta('B_LUGGAGE_PUB', 0, None, None, 0)
#Gender
B_MALE_TRAIN = Beta('B_MALE_TRAIN', 0, None, None, 0)
B_MALE_SM = Beta('B_MALE_SM', 0, None, None, 0)
B_MALE_PUB = Beta('B_MALE_PUB', 0, None, None, 0)
#Purpose
B_BUSINESS = Beta('B_BUSINESS', 0, None, None, 0)
B_BUSINESS_TRAIN = Beta('B_BUSINESS_TRAIN', 0, None, None, 0)
B_BUSINESS_SM = Beta('B_BUSINESS_SM', 0, None, None, 0)
B_PRIVATE = Beta('B_PRIVATE', 0, None, None, 0)
B_PRIVATE_TRAIN = Beta('B_PRIVATE_TRAIN', 0, None, None, 0)
B_PRIVATE_SM = Beta('B_PRIVATE_SM', 0, None, None, 0)
B_COMMUTER = Beta('B_COMMUTER', 0, None, None, 0)
B_COMMUTER_TRAIN = Beta('B_COMMUTER_TRAIN', 0, None, None, 0)
B_COMMUTER_SM = Beta('B_COMMUTER_SM', 0, None, None, 0)
#GA
B_GA = Beta('B_GA', 0, None, None, 0)
B_GA_TRAIN = Beta('B_GA_TRAIN', 0, None, None, 0)
B_GA_SM = Beta('B_GA_SM', 0, None, None, 0)
#First
B_FIRST_TRAIN = Beta('B_FIRST_TRAIN', 0, None, None, 0)
B_FIRST_SM = Beta('B_FIRST_SM', 0, None, None, 0)
B_FIRST = Beta('B_FIRST', 0, None, None, 0)
##Non linearization
#Cost
q_COST = Beta('q_COST', 1, None, None, 0)
#Time
q_TIME = Beta('q_TIME', 1, None, None, 0)
q_TIME_TRAIN = Beta('q_TIME_TRAIN', 1, None, None, 0)
q_TIME_SM = Beta('q_TIME_SM', 1, None, None, 0)
q_TIME_CAR = Beta('q_TIME_CAR', 1, None, None, 0)
q_TIME_PUB = Beta('q_TIME_PUB', 1, None, None, 0)
#HE
q_HE = Beta('q_HE', 1, None, None, 0)
##Nesting parameter
MU = Beta('MU', 1, 0, 1, 0)
##ML RANDOM GENERIC TIME LOGNORMAL
BETA_TIME_mean = Beta('BETA_TIME_mean', 0, None, None, 0)
BETA_TIME_std = Beta('BETA_TIME_std', 1, None, None, 0)
BETA_TIME_random = -exp(BETA_TIME_mean + BETA_TIME_std *
bioDraws('BETA_TIME_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean = Beta('BETA_TIME_TRAIN_mean', 0, None, None, 0)
BETA_TIME_TRAIN_std = Beta('BETA_TIME_TRAIN_std', 1, None, None, 0)
BETA_TIME_TRAIN_random = -exp(BETA_TIME_TRAIN_mean + BETA_TIME_TRAIN_std *
bioDraws('BETA_TIME_TRAIN_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME SM LOGNORMAL
BETA_TIME_SM_mean = Beta('BETA_TIME_SM_mean', 0, None, None, 0)
BETA_TIME_SM_std = Beta('BETA_TIME_SM_std', 1, None, None, 0)
BETA_TIME_SM_random = -exp(BETA_TIME_SM_mean + BETA_TIME_SM_std *
bioDraws('BETA_TIME_SM_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME CAR LOGNORMAL
BETA_TIME_CAR_mean = Beta('BETA_TIME_CAR_mean', 0, None, None, 0)
BETA_TIME_CAR_std = Beta('BETA_TIME_CAR_std', 1, None, None, 0)
BETA_TIME_CAR_random = -exp(BETA_TIME_CAR_mean + BETA_TIME_CAR_std *
bioDraws('BETA_TIME_CAR_random', 'NORMAL'))
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_mean = Beta('BETA_COST_mean', 0, None, None, 0)
BETA_COST_std = Beta('BETA_COST_std', 1, None, None, 0)
BETA_COST_random = -exp(BETA_COST_mean + BETA_COST_std *
bioDraws('BETA_COST_random', 'NORMAL'))
##ML RANDOM GENERIC HE LOGNORMAL
BETA_HE_mean = Beta('BETA_HE_mean', 0, None, None, 0)
BETA_HE_std = Beta('BETA_HE_std', 1, None, None, 0)
BETA_HE_random = -exp(BETA_HE_mean +
BETA_HE_std * bioDraws('BETA_HE_random', 'NORMAL'))
##ML RANDOM GENERIC TIME NORMAL
BETA_TIME_mean_Norm = Beta('BETA_TIME_mean_Norm', 0, None, None, 0)
BETA_TIME_std_Norm = Beta('BETA_TIME_std_Norm', 1, None, None, 0)
BETA_TIME_random_Norm = BETA_TIME_mean_Norm + BETA_TIME_std_Norm * bioDraws(
'BETA_TIME_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean_Norm = Beta('BETA_TIME_TRAIN_mean_Norm', 0, None,
None, 0)
BETA_TIME_TRAIN_std_Norm = Beta('BETA_TIME_TRAIN_std_Norm', 1, None, None,
0)
BETA_TIME_TRAIN_random_Norm = BETA_TIME_TRAIN_mean_Norm + BETA_TIME_TRAIN_std_Norm * bioDraws(
'BETA_TIME_TRAIN_random_Norm', 'NORMAL')
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_PUB_mean = Beta('BBETA_COST_PUB_mean', 0, None, None, 0)
BETA_COST_PUB_std = Beta('BBETA_COST_PUB_std', 1, None, None, 0)
BETA_COST_PUB_random = -exp(BETA_COST_PUB_mean + BETA_COST_PUB_std *
bioDraws('BBETA_COST_PUB_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME SM NORMAL
BETA_TIME_SM_mean_Norm = Beta('BETA_TIME_SM_mean_Norm', 0, None, None, 0)
BETA_TIME_SM_std_Norm = Beta('BETA_TIME_SM_std_Norm', 1, None, None, 0)
BETA_TIME_SM_random_Norm = BETA_TIME_SM_mean_Norm + BETA_TIME_SM_std_Norm * bioDraws(
'BETA_TIME_SM_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME CAR NORMAL
BETA_TIME_CAR_mean_Norm = Beta('BETA_TIME_CAR_mean_Norm', 0, None, None, 0)
BETA_TIME_CAR_std_Norm = Beta('BETA_TIME_CAR_std_Norm', 1, None, None, 0)
BETA_TIME_CAR_random_Norm = BETA_TIME_CAR_mean_Norm + BETA_TIME_CAR_std_Norm * bioDraws(
'BETA_TIME_CAR_random_Norm', 'NORMAL')
##ML RANDOM GENERIC COST NORMAL
BETA_COST_mean_Norm = Beta('BETA_COST_mean_Norm', 0, None, None, 0)
BETA_COST_std_Norm = Beta('BETA_COST_std_Norm', 1, None, None, 0)
BETA_COST_random_Norm = BETA_COST_mean_Norm + BETA_COST_std_Norm * bioDraws(
'BETA_COST_random_Norm', 'NORMAL')
##ML RANDOM GENERIC HE NORMAL
BETA_HE_mean_Norm = Beta('BETA_HE_mean_Norm', 0, None, None, 0)
BETA_HE_std_Norm = Beta('BETA_HE_std_Norm', 1, None, None, 0)
BETA_HE_random_Norm = BETA_HE_mean_Norm + BETA_HE_std_Norm * bioDraws(
'BETA_HE_random_Norm', 'NORMAL')
##ML RANDOM GENERIC TIME NORMAL
BBETA_TIME_mean_Norm = Beta('BBETA_TIME_mean_Norm', 0, None, None, 0)
BBETA_TIME_std_Norm = Beta('BBETA_TIME_std_Norm', 1, None, None, 0)
BBETA_TIME_random_Norm = BBETA_TIME_mean_Norm + BBETA_TIME_std_Norm * bioDraws(
'BBETA_TIME_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BBETA_TIME_TRAIN_mean_Norm = Beta('BBETA_TIME_TRAIN_mean_Norm', 0, None,
None, 0)
BBETA_TIME_TRAIN_std_Norm = Beta('BBETA_TIME_TRAIN_std_Norm', 1, None,
None, 0)
BBETA_TIME_TRAIN_random_Norm = BBETA_TIME_TRAIN_mean_Norm + BBETA_TIME_TRAIN_std_Norm * bioDraws(
'BBETA_TIME_TRAIN_random_Norm', 'NORMAL')
##Scaling 'COST', 'TRAVEL-TIME' and 'HE' by a factor of 100 and adding the scaled variables to the database
'''
***********************************************************************************************
'''
#PUBLIC
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
TRAIN_COST / COST_SCALE_PUB,db_test)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / COST_SCALE_PUB,
db_test)
#CAR
CAR_COST_SCALED = DefineVariable('CAR_COST_SCALED',
CAR_CO / COST_SCALE_CAR, db_test)
'''
***********************************************************************************************
'''
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
TRAIN_TT / 100.0,db_test)
TRAIN_HE_SCALED = DefineVariable('TRAIN_HE_SCALED',\
TRAIN_HE / 100, db_test)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0, db_test)
SM_HE_SCALED = DefineVariable('SM_HE_SCALED',\
SM_HE / 100, db_test)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100, db_test)
###Defining new variables and adding columns to the database
#Age
AGE_1 = DefineVariable('AGE_1', (AGE == 1),
db_test) #don't scale because is cathegorical
AGE_2 = DefineVariable('AGE_2', (AGE == 2), db_test)
AGE_3 = DefineVariable('AGE_3', (AGE == 3), db_test)
AGE_4 = DefineVariable('AGE_4', (AGE == 4), db_test)
AGE_5 = DefineVariable('AGE_5', (AGE == 5), db_test)
AGE_6 = DefineVariable('AGE_6', (AGE == 6), db_test)
#Purpose
PRIVATE = DefineVariable("PRIVATE", (PURPOSE == 1), db_test)
COMMUTER = DefineVariable("COMMUTER", (PURPOSE == 2), db_test)
BUSINESS = DefineVariable("BUSINESS", (PURPOSE == 3), db_test)
av = {3: CAR_AV_SP, 1: TRAIN_AV_SP, 2: SM_AV}
prob1 = biogeme.expressions.MonteCarlo(exp(models.loglogit(V, av, 1)))
prob2 = biogeme.expressions.MonteCarlo(exp(models.loglogit(V, av, 2)))
prob3 = biogeme.expressions.MonteCarlo(exp(models.loglogit(V, av, 3)))
simulate = {'Prob. TRAIN': prob1, 'Prob. SM': prob2, 'Prob. CAR': prob3}
#print(simulate)
biosim = bio.BIOGEME(db_test, simulate, numberOfDraws=Draws)
biosim.modelName = ModName
SR = biosim.simulate(R.data.betaValues)
return SR
def cv_estimate_model(V,
Draws,
ModName,
train,
myRandomNumberGenerators,
COST_SCALE_CAR=100,
COST_SCALE_PUB=100):
db_train = db.Database("swissmetro_train", train)
db_train.setRandomNumberGenerators(myRandomNumberGenerators)
globals().update(db_train.variables)
#locals().update(db_train.variables)
db_train.panel('ID')
#variables
CAR_AV_SP = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), db_train)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0), db_train)
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
###Parameters to be estimated (Note not all parameters are used in all models!)
##Attributes
#Alternative specific constants
ASC_CAR = Beta('ASC_CAR', 0, None, None, 1)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 0)
#Cost (Note: Assumed generic)
B_COST = Beta('B_COST', 0, None, None, 0)
B_COST_BUSINESS = Beta('B_COST_BUSINESS', 0, None, None, 0)
B_COST_PRIVATE = Beta('B_COST_PRIVATE', 0, None, None, 0)
#Time
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_TIME_CAR = Beta('B_TIME_CAR', 0, None, None, 0)
B_TIME_TRAIN = Beta('B_TIME_TRAIN', 0, None, None, 0)
B_TIME_SM = Beta('B_TIME_SM', 0, None, None, 0)
B_TIME_PUB = Beta('B_TIME_PUB', 0, None, None, 0)
B_TIME_CAR_BUSINESS = Beta('B_TIME_CAR_BUSINESS', 0, None, None, 0)
B_TIME_TRAIN_BUSINESS = Beta('B_TIME_TRAIN_BUSINESS', 0, None, None, 0)
B_TIME_SM_BUSINESS = Beta('B_TIME_SM_BUSINESS', 0, None, None, 0)
B_TIME_PUB_BUSINESS = Beta('B_TIME_PUB_BUSINESS', 0, None, None, 0)
B_TIME_CAR_PRIVATE = Beta('B_TIME_CAR_PRIVATE', 0, None, None, 0)
B_TIME_TRAIN_PRIVATE = Beta('B_TIME_TRAIN_PRIVATE', 0, None, None, 0)
B_TIME_SM_PRIVATE = Beta('B_TIME_SM_PRIVATE', 0, None, None, 0)
B_TIME_PUB_PRIVATE = Beta('B_TIME_PUB_PRIVATE', 0, None, None, 0)
#HE (Note: Not available for car)
B_HE = Beta('B_HE', 0, None, None, 0)
B_HE_TRAIN = Beta('B_HE_TRAIN', 0, None, None, 0)
B_HE_SM = Beta('B_HE_SM', 0, None, None, 0)
B_HE_BUSINESS = Beta('B_HE_BUSINESS', 0, None, None, 0)
B_HE_TRAIN_BUSINESS = Beta('B_HE_TRAIN_BUSINESS', 0, None, None, 0)
B_HE_SM_BUSINESS = Beta('B_HE_SM_BUSINESS', 0, None, None, 0)
B_HE_PRIVATE = Beta('B_HE_PRIVATE', 0, None, None, 0)
B_HE_TRAIN_PRIVATE = Beta('B_HE_TRAIN_PRIVATE', 0, None, None, 0)
B_HE_SM_PRIVATE = Beta('B_HE_SM_PRIVATE', 0, None, None, 0)
#Seats (Note: Only avaliable for SM)
B_SEATS = Beta('B_SEATS', 0, None, None, 0)
##Characteristics
#Age
B_AGE_1_TRAIN = Beta('B_AGE_1_TRAIN', 0, None, None, 1) #Note: Reference
B_AGE_2_TRAIN = Beta('B_AGE_2_TRAIN', 0, None, None, 0)
B_AGE_3_TRAIN = Beta('B_AGE_3_TRAIN', 0, None, None, 0)
B_AGE_4_TRAIN = Beta('B_AGE_4_TRAIN', 0, None, None, 0)
B_AGE_5_TRAIN = Beta('B_AGE_5_TRAIN', 0, None, None, 0)
B_AGE_6_TRAIN = Beta('B_AGE_6_TRAIN', 0, None, None, 0)
B_AGE_1_SM = Beta('B_AGE_1_SM', 0, None, None, 1) #Note: Reference
B_AGE_2_SM = Beta('B_AGE_2_SM', 0, None, None, 0)
B_AGE_3_SM = Beta('B_AGE_3_SM', 0, None, None, 0)
B_AGE_4_SM = Beta('B_AGE_4_SM', 0, None, None, 0)
B_AGE_5_SM = Beta('B_AGE_5_SM', 0, None, None, 0)
B_AGE_6_SM = Beta('B_AGE_6_SM', 0, None, None, 0)
B_AGE_1_PUB = Beta('B_AGE_1_PUB', 0, None, None, 1) #Note: Reference
B_AGE_2_PUB = Beta('B_AGE_2_PUB', 0, None, None, 0)
B_AGE_3_PUB = Beta('B_AGE_3_PUB', 0, None, None, 0)
B_AGE_4_PUB = Beta('B_AGE_4_PUB', 0, None, None, 0)
B_AGE_5_PUB = Beta('B_AGE_5_PUB', 0, None, None, 0)
B_AGE_6_PUB = Beta('B_AGE_6_PUB', 0, None, None, 0)
B_AGE_ADULTS_TRAIN = Beta('B_AGE_TRAIN_ADULTS', 0, None, None, 0)
B_AGE_ADULTS_SM = Beta('B_AGE_ADULTS_SM', 0, None, None, 0)
B_AGE_ADULTS_PUB = Beta('B_AGE_ADULTS_PUB', 0, None, None, 0)
#Luggage
B_LUGGAGE_TRAIN = Beta('B_LUGGAGE_TRAIN', 0, None, None, 0)
B_LUGGAGE_SM = Beta('B_LUGGAGE_SM', 0, None, None, 0)
B_LUGGAGE_PUB = Beta('B_LUGGAGE_PUB', 0, None, None, 0)
#Gender
B_MALE_TRAIN = Beta('B_MALE_TRAIN', 0, None, None, 0)
B_MALE_SM = Beta('B_MALE_SM', 0, None, None, 0)
B_MALE_PUB = Beta('B_MALE_PUB', 0, None, None, 0)
#Purpose
B_BUSINESS = Beta('B_BUSINESS', 0, None, None, 0)
B_BUSINESS_TRAIN = Beta('B_BUSINESS_TRAIN', 0, None, None, 0)
B_BUSINESS_SM = Beta('B_BUSINESS_SM', 0, None, None, 0)
B_PRIVATE = Beta('B_PRIVATE', 0, None, None, 0)
B_PRIVATE_TRAIN = Beta('B_PRIVATE_TRAIN', 0, None, None, 0)
B_PRIVATE_SM = Beta('B_PRIVATE_SM', 0, None, None, 0)
B_COMMUTER = Beta('B_COMMUTER', 0, None, None, 0)
B_COMMUTER_TRAIN = Beta('B_COMMUTER_TRAIN', 0, None, None, 0)
B_COMMUTER_SM = Beta('B_COMMUTER_SM', 0, None, None, 0)
#GA
B_GA = Beta('B_GA', 0, None, None, 0)
B_GA_TRAIN = Beta('B_GA_TRAIN', 0, None, None, 0)
B_GA_SM = Beta('B_GA_SM', 0, None, None, 0)
#First
B_FIRST_TRAIN = Beta('B_FIRST_TRAIN', 0, None, None, 0)
B_FIRST_SM = Beta('B_FIRST_SM', 0, None, None, 0)
B_FIRST = Beta('B_FIRST', 0, None, None, 0)
##Non linearization
#Cost
q_COST = Beta('q_COST', 1, None, None, 0)
#Time
q_TIME = Beta('q_TIME', 1, None, None, 0)
q_TIME_TRAIN = Beta('q_TIME_TRAIN', 1, None, None, 0)
q_TIME_SM = Beta('q_TIME_SM', 1, None, None, 0)
q_TIME_CAR = Beta('q_TIME_CAR', 1, None, None, 0)
q_TIME_PUB = Beta('q_TIME_PUB', 1, None, None, 0)
#HE
q_HE = Beta('q_HE', 1, None, None, 0)
##Nesting parameter
MU = Beta('MU', 1, 0, 1, 0)
##ML RANDOM GENERIC TIME LOGNORMAL
BETA_TIME_mean = Beta('BETA_TIME_mean', 0, None, None, 0)
BETA_TIME_std = Beta('BETA_TIME_std', 1, None, None, 0)
BETA_TIME_random = -exp(BETA_TIME_mean + BETA_TIME_std *
bioDraws('BETA_TIME_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean = Beta('BETA_TIME_TRAIN_mean', 0, None, None, 0)
BETA_TIME_TRAIN_std = Beta('BETA_TIME_TRAIN_std', 1, None, None, 0)
BETA_TIME_TRAIN_random = -exp(BETA_TIME_TRAIN_mean + BETA_TIME_TRAIN_std *
bioDraws('BETA_TIME_TRAIN_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME SM LOGNORMAL
BETA_TIME_SM_mean = Beta('BETA_TIME_SM_mean', 0, None, None, 0)
BETA_TIME_SM_std = Beta('BETA_TIME_SM_std', 1, None, None, 0)
BETA_TIME_SM_random = -exp(BETA_TIME_SM_mean + BETA_TIME_SM_std *
bioDraws('BETA_TIME_SM_random', 'NORMAL'))
##ML RANDOM SPECIFIC TIME CAR LOGNORMAL
BETA_TIME_CAR_mean = Beta('BETA_TIME_CAR_mean', 0, None, None, 0)
BETA_TIME_CAR_std = Beta('BETA_TIME_CAR_std', 1, None, None, 0)
BETA_TIME_CAR_random = -exp(BETA_TIME_CAR_mean + BETA_TIME_CAR_std *
bioDraws('BETA_TIME_CAR_random', 'NORMAL'))
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_mean = Beta('BETA_COST_mean', 0, None, None, 0)
BETA_COST_std = Beta('BETA_COST_std', 1, None, None, 0)
BETA_COST_random = -exp(BETA_COST_mean + BETA_COST_std *
bioDraws('BETA_COST_random', 'NORMAL'))
##ML RANDOM GENERIC HE LOGNORMAL
BETA_HE_mean = Beta('BETA_HE_mean', 0, None, None, 0)
BETA_HE_std = Beta('BETA_HE_std', 1, None, None, 0)
BETA_HE_random = -exp(BETA_HE_mean +
BETA_HE_std * bioDraws('BETA_HE_random', 'NORMAL'))
##ML RANDOM GENERIC TIME NORMAL
BETA_TIME_mean_Norm = Beta('BETA_TIME_mean_Norm', 0, None, None, 0)
BETA_TIME_std_Norm = Beta('BETA_TIME_std_Norm', 1, None, None, 0)
BETA_TIME_random_Norm = BETA_TIME_mean_Norm + BETA_TIME_std_Norm * bioDraws(
'BETA_TIME_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME TRAIN LOGNORMAL
BETA_TIME_TRAIN_mean_Norm = Beta('BETA_TIME_TRAIN_mean_Norm', 0, None,
None, 0)
BETA_TIME_TRAIN_std_Norm = Beta('BETA_TIME_TRAIN_std_Norm', 1, None, None,
0)
BETA_TIME_TRAIN_random_Norm = BETA_TIME_TRAIN_mean_Norm + BETA_TIME_TRAIN_std_Norm * bioDraws(
'BETA_TIME_TRAIN_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME SM NORMAL
BETA_TIME_SM_mean_Norm = Beta('BETA_TIME_SM_mean_Norm', 0, None, None, 0)
BETA_TIME_SM_std_Norm = Beta('BETA_TIME_SM_std_Norm', 1, None, None, 0)
BETA_TIME_SM_random_Norm = BETA_TIME_SM_mean_Norm + BETA_TIME_SM_std_Norm * bioDraws(
'BETA_TIME_SM_random_Norm', 'NORMAL')
##ML RANDOM SPECIFIC TIME CAR NORMAL
BETA_TIME_CAR_mean_Norm = Beta('BETA_TIME_CAR_mean_Norm', 0, None, None, 0)
BETA_TIME_CAR_std_Norm = Beta('BETA_TIME_CAR_std_Norm', 1, None, None, 0)
BETA_TIME_CAR_random_Norm = BETA_TIME_CAR_mean_Norm + BETA_TIME_CAR_std_Norm * bioDraws(
'BETA_TIME_CAR_random_Norm', 'NORMAL')
##ML RANDOM GENERIC COST LOGNORMAL
BETA_COST_PUB_mean = Beta('BBETA_COST_PUB_mean', 0, None, None, 0)
BETA_COST_PUB_std = Beta('BBETA_COST_PUB_std', 1, None, None, 0)
BETA_COST_PUB_random = -exp(BETA_COST_PUB_mean + BETA_COST_PUB_std *
bioDraws('BBETA_COST_PUB_random', 'NORMAL'))
##ML RANDOM GENERIC COST NORMAL
BETA_COST_mean_Norm = Beta('BETA_COST_mean_Norm', 0, None, None, 0)
BETA_COST_std_Norm = Beta('BETA_COST_std_Norm', 1, None, None, 0)
BETA_COST_random_Norm = BETA_COST_mean_Norm + BETA_COST_std_Norm * bioDraws(
'BETA_COST_random_Norm', 'NORMAL')
##ML RANDOM GENERIC HE NORMAL
BETA_HE_mean_Norm = Beta('BETA_HE_mean_Norm', 0, None, None, 0)
BETA_HE_std_Norm = Beta('BETA_HE_std_Norm', 1, None, None, 0)
BETA_HE_random_Norm = BETA_HE_mean_Norm + BETA_HE_std_Norm * bioDraws(
'BETA_HE_random_Norm', 'NORMAL')
'''
***********************************************************************************************
'''
#PUBLIC
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
TRAIN_COST / COST_SCALE_PUB,db_train)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / COST_SCALE_PUB,
db_train)
#CAR
CAR_COST_SCALED = DefineVariable('CAR_COST_SCALED',
CAR_CO / COST_SCALE_CAR, db_train)
'''
***********************************************************************************************
'''
##Scaling 'COST', 'TRAVEL-TIME' and 'HE' by a factor of 100 and adding the scaled variables to the database
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
TRAIN_TT / 100.0,db_train)
TRAIN_HE_SCALED = DefineVariable('TRAIN_HE_SCALED',\
TRAIN_HE / 100, db_train)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0, db_train)
SM_HE_SCALED = DefineVariable('SM_HE_SCALED',\
SM_HE / 100, db_train)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100, db_train)
###Defining new variables and adding columns to the database
#Age
AGE_1 = DefineVariable('AGE_1', (AGE == 1),
db_train) #don't scale because is cathegorical
AGE_2 = DefineVariable('AGE_2', (AGE == 2), db_train)
AGE_3 = DefineVariable('AGE_3', (AGE == 3), db_train)
AGE_4 = DefineVariable('AGE_4', (AGE == 4), db_train)
AGE_5 = DefineVariable('AGE_5', (AGE == 5), db_train)
AGE_6 = DefineVariable('AGE_6', (AGE == 6), db_train)
#Purpose
PRIVATE = DefineVariable("PRIVATE", (PURPOSE == 1), db_train)
COMMUTER = DefineVariable("COMMUTER", (PURPOSE == 2), db_train)
BUSINESS = DefineVariable("BUSINESS", (PURPOSE == 3), db_train)
#Model Estimation
av = {3: CAR_AV_SP, 1: TRAIN_AV_SP, 2: SM_AV}
obsprob = exp(models.loglogit(V, av, CHOICE))
condprobIndiv = PanelLikelihoodTrajectory(obsprob)
logprob = log(MonteCarlo(condprobIndiv))
bg = bio.BIOGEME(db_train, logprob, numberOfDraws=Draws)
bg.modelName = ModName
result = bg.estimate()
return result
9: 'Group ticket',
10: 'Other'
}
#load data into database
database = db.Database("swissmetro", pandas)
globals().update(database.variables)
database.panel('ID')
## Define the draw generator
myRandomNumberGenerators = {'MLHS': draws.getLatinHypercubeDraws}
database.setRandomNumberGenerators(myRandomNumberGenerators)
#define variables
#variables
CAR_AV_SP = DefineVariable('CAR_AV_SP', CAR_AV * (SP != 0), database)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP', TRAIN_AV * (SP != 0), database)
SM_COST = SM_CO * (GA == 0)
TRAIN_COST = TRAIN_CO * (GA == 0)
###Parameters to be estimated (Note not all parameters are used in all models!)
##Attributes
#Alternative specific constants
ASC_CAR = Beta('ASC_CAR', 0, None, None, 1)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 0)
#Cost (Note: Assumed generic)
B_COST = Beta('B_COST', 0, None, None, 0)
B_COST_BUSINESS = Beta('B_COST_BUSINESS', 0, None, None, 0)
B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + \
B_TIME * SM_TT_SCALED + \
B_COST * SM_COST_SCALED
V3 = ASC_CAR + \
B_TIME * CAR_TT_SCALED + \
B_COST * CAR_CO_SCALED
# Associate utility functions with the numbering of alternatives
V = {1: V1,
2: V2,
3: V3}
# Associate the availability conditions with the alternatives
CAR_AV_SP = DefineVariable('CAR_AV_SP',CAR_AV * ( SP != 0 ),database)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP',TRAIN_AV * ( SP != 0 ),database)
av = {1: TRAIN_AV_SP,
2: SM_AV,
3: CAR_AV_SP}
# The choice model is a logit, with availability conditions
prob1 = Elem({0:0,1:models.logit(V,av,1)},av[1])
# Elasticities can be computed. We illustrate below two
# formulas. Check in the output file that they produce the same
# result.
# First, the general definition of elasticities. This illustrates the
# use of the Derive expression, and can be used with any model,
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)