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 compliment to the application in Trip Distribution.

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 [ORW2011].

Imports

import numpy as np

from aequilibrae.distribution import Ipf
from os.path import join
from tempfile import gettempdir
from aequilibrae.matrix import AequilibraeMatrix, AequilibraeData

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 = AequilibraeData()
vectors.create_empty(**args)

vectors.productions[:] = future_prod[:]
vectors.attractions[:] = future_attr[:]

vectors.index[:] = mtx.index[:]

args = {
    "matrix": mtx,
    "rows": vectors,
    "row_field": "productions",
    "columns": vectors,
    "column_field": "attractions",
    "nan_as_zero": True,
}
fratar = Ipf(**args)
fratar.fit()

fratar.output.matrix_view

for line in fratar.report:
    print(line)