Note
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Cross point elasticities
We use a previously estimated nested logit model and calculate disaggregate and aggregate cross point elasticities.
Details about this example are available in Section 3 of Bierlaire (2018) Calculating indicators with PandasBiogeme
- author:
Michel Bierlaire, EPFL
- date:
Wed Apr 12 21:00:58 2023
import sys
import biogeme.biogeme as bio
from biogeme import models
import biogeme.exceptions as excep
import biogeme.results as res
from biogeme.expressions import Derive
from optima_data import database, normalized_weight
from scenarios import (
scenario,
TimePT,
TimeCar,
MarginalCostPT,
CostCarCHF,
)
Obtain the specification for the default scenario The definition of the scenarios is available in Specification of a nested logit model.
V, nests, _, _ = scenario()
Obtain the expression for the choice probability of each alternative.
prob_PT = models.nested(V, None, nests, 0)
prob_CAR = models.nested(V, None, nests, 1)
prob_SM = models.nested(V, None, nests, 2)
The choice model is a nested logit.
prob_PT = models.nested(V, None, nests, 0)
prob_CAR = models.nested(V, None, nests, 1)
prob_SM = models.nested(V, None, nests, 2)
Calculation of the cross elasticities. We use the ‘Derive’ operator to calculate the derivatives.
cross_elas_pt_time = Derive(prob_PT, 'TimeCar') * TimeCar / prob_PT
cross_elas_pt_cost = Derive(prob_PT, 'CostCarCHF') * CostCarCHF / prob_PT
cross_elas_car_time = Derive(prob_CAR, 'TimePT') * TimePT / prob_CAR
cross_elas_car_cost = Derive(prob_CAR, 'MarginalCostPT') * MarginalCostPT / prob_CAR
Formulas to simulate.
simulate = {
'weight': normalized_weight,
'Prob. car': prob_CAR,
'Prob. public transportation': prob_PT,
'Prob. slow modes': prob_SM,
'cross_elas_pt_time': cross_elas_pt_time,
'cross_elas_pt_cost': cross_elas_pt_cost,
'cross_elas_car_time': cross_elas_car_time,
'cross_elas_car_cost': cross_elas_car_cost,
}
Create the Biogeme object.
the_biogeme = bio.BIOGEME(database, simulate)
Read the estimation results from the file
try:
results = res.bioResults(pickleFile='saved_results/b02estimation.pickle')
except excep.BiogemeError:
sys.exit(
'Run first the script b02estimation.py in order to generate '
'the file b02estimation.pickle.'
)
simulated_values is a Panda dataframe with the same number of rows as the database, and as many columns as formulas to simulate.
simulated_values = the_biogeme.simulate(results.getBetaValues())
simulated_values
We calculate the aggregate elasticities.
First, the weighted probabilities.
simulated_values['Weighted prob. car'] = (
simulated_values['weight'] * simulated_values['Prob. car']
)
simulated_values['Weighted prob. PT'] = (
simulated_values['weight'] * simulated_values['Prob. public transportation']
)
Then the denominators of the aggregate elasticity expressions.
denominator_car = simulated_values['Weighted prob. car'].sum()
denominator_pt = simulated_values['Weighted prob. PT'].sum()
And finally the aggregate elasticities themselves.
Elasticity of car with respect to public transportation travel time.
cross_elas_term_car_time = (
simulated_values['Weighted prob. car']
* simulated_values['cross_elas_car_time']
/ denominator_car
).sum()
print(
f'Aggregate cross elasticity of car wrt PT time: ' f'{cross_elas_term_car_time:.3g}'
)
Aggregate cross elasticity of car wrt PT time: 0.107
Elasticity of car with respect to public transportation travel cost.
cross_elas_term_car_cost = (
simulated_values['Weighted prob. car']
* simulated_values['cross_elas_car_cost']
/ denominator_car
).sum()
print(
f'Aggregate cross elasticity of car wrt PT cost: ' f'{cross_elas_term_car_cost:.3g}'
)
Aggregate cross elasticity of car wrt PT cost: 0.123
Elasticity of public transportatiom with respect to car travel time.
cross_elas_term_pt_time = (
simulated_values['Weighted prob. PT']
* simulated_values['cross_elas_pt_time']
/ denominator_pt
).sum()
print(
f'Aggregate cross elasticity of PT wrt car time: ' f'{cross_elas_term_pt_time:.3g}'
)
Aggregate cross elasticity of PT wrt car time: 0.0953
Elasticity of public transportatiom with respect to car travel cost.
cross_elas_term_pt_cost = (
simulated_values['Weighted prob. PT']
* simulated_values['cross_elas_pt_cost']
/ denominator_pt
).sum()
print(
f'Aggregate cross direct elasticity of PT wrt car cost: '
f'{cross_elas_term_pt_cost:.3g}'
)
Aggregate cross direct elasticity of PT wrt car cost: 0.2
Total running time of the script: (0 minutes 1.187 seconds)