aequilibrae.distribution.gravity_calibration#

Algorithms to calibrate synthetic gravity models with power and exponential functions

The procedures implemented in this code are some of those suggested in Modelling Transport, 4th Edition, Ortuzar and Willumsen, Wiley 2011

Classes

GravityCalibration([project])

Calibrate a traditional gravity model
class aequilibrae.distribution.gravity_calibration.GravityCalibration(project=None, **kwargs)[source]#

Calibrate a traditional gravity model

Available deterrence function forms are: ‘EXPO’, ‘POWER’ or ‘GAMMA’.

>>> from aequilibrae.distribution import GravityCalibration

>>> project = create_example(project_path)

# We load the demand matrix
>>> demand = project.matrices.get_matrix("demand_omx")
>>> demand.computational_view()

# We load the skim matrix
>>> skim = project.matrices.get_matrix("skims")
>>> skim.computational_view(["time_final"])

>>> args = {"matrix": demand,
...         "impedance": skim,
...         "row_field": "productions",
...         "function": 'expo',
...         "nan_as_zero": True}
>>> gravity = GravityCalibration(**args)

# Solve and save outputs
>>> gravity.calibrate()
>>> gravity.model.save(project_path / 'dist_expo_model.mod')

>>> project.close()
calibrate()[source]#

Calibrate the model

Resulting model is in output class member