Note
Go to the end to download the full example code.
Running IPF without an AequilibraE model#
In this example, we show you how to use AequilibraE’s IPF function without a model. This is a complement to the application in Forecasting.
Let’s consider that you have an OD-matrix, the future production and future attraction values.
How would your trip distribution matrix using IPF look like?
The data used in this example comes from Table 5.6 in Ortúzar & Willumsen (2011).
References
See also
Several functions, methods, classes and modules are used in this example:
# Imports
from os.path import join
from tempfile import gettempdir
import numpy as np
import pandas as pd
from aequilibrae.distribution import Ipf
from aequilibrae.matrix import AequilibraeMatrix
folder = gettempdir()
matrix = np.array([[5, 50, 100, 200], [50, 5, 100, 300], [50, 100, 5, 100], [100, 200, 250, 20]], dtype="float64")
future_prod = np.array([400, 460, 400, 702], dtype="float64")
future_attr = np.array([260, 400, 500, 802], dtype="float64")
num_zones = matrix.shape[0]
mtx = AequilibraeMatrix()
mtx.create_empty(file_name=join(folder, "matrix.aem"), zones=num_zones)
mtx.index[:] = np.arange(1, num_zones + 1)[:]
mtx.matrices[:, :, 0] = matrix[:]
mtx.computational_view()
args = {
"entries": mtx.index.shape[0],
"field_names": ["productions", "attractions"],
"data_types": [np.float64, np.float64],
"file_path": join(folder, "vectors.aem"),
}
vectors = pd.DataFrame({"productions": future_prod, "attractions": future_attr}, index=mtx.index)
args = {
"matrix": mtx,
"vectors": vectors,
"row_field": "productions",
"column_field": "attractions",
"nan_as_zero": True,
}
fratar = Ipf(**args)
fratar.fit()
fratar.output.matrix_view
for line in fratar.report:
print(line)