Note
Go to the end to download the full example code.
Public transport assignment with skimming#
In this example, we build on the transit assignment example and add skimming to it.
We use data from Coquimbo, a city in La Serena Metropolitan Area in Chile.
References
WE HIGHLY RECOMMEND YOU READ THE DOCUMENTATION ON SKIMMING BEFORE PROCEEDING
See also
Several functions, methods, classes and modules are used in this example:
Imports for example construction
from os.path import join
from tempfile import gettempdir
from uuid import uuid4
import numpy as np
from aequilibrae.matrix import AequilibraeMatrix
from aequilibrae.paths import TransitAssignment, TransitClass
from aequilibrae.project.database_connection import database_connection
from aequilibrae.transit import Transit
from aequilibrae.transit.transit_graph_builder import TransitGraphBuilder
from aequilibrae.utils.create_example import create_example
# Let's create an empty project on an arbitrary folder.
fldr = join(gettempdir(), uuid4().hex)
project = create_example(fldr, "coquimbo")
Let’s create our Transit object.
data = Transit(project)
Graph building#
Let’s build the transit network. We’ll disable outer_stop_transfers and walking_edges
because Coquimbo doesn’t have any parent stations.
For the OD connections we’ll use the overlapping_regions method and create some accurate line geometry later.
Creating the graph should only take a moment. By default zoning information is pulled from the project network.
If you have your own zoning information add it using graph.add_zones(zones) then graph.create_graph().
graph = data.create_graph(
with_outer_stop_transfers=False,
with_walking_edges=False,
blocking_centroid_flows=False,
connector_method="overlapping_regions"
)
Connector project matching#
project.network.build_graphs()
graph.create_line_geometry(method="connector project match", graph="c")
data.save_graphs()
data.load()
# Reading back into AequilibraE
pt_con = database_connection("transit")
graph_db = TransitGraphBuilder.from_db(pt_con, project.network.periods.default_period.period_id)
graph_db.vertices.drop(columns="geometry")
# To perform an assignment we need to convert the graph builder into a graph.
transit_graph = graph_db.to_transit_graph()
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
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warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
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warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/transit/transit_graph_builder.py:1191: UserWarning: In its current implementation, the "connector project match" method may take a while for large networks.
warnings.warn(
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
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warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
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121 122 123 124 125 126 127 128 129 130 131 132 133]
warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))
# Mock demand matrix
zones = len(transit_graph.centroids)
mat = AequilibraeMatrix()
mat.create_empty(zones=zones, matrix_names=['pt'], memory_only=True)
mat.index = transit_graph.centroids[:]
mat.matrices[:, :, 0] = np.full((zones, zones), 1.0)
mat.computational_view()
Hyperpath generation/assignment#
We’ll create a TransitAssignment object as well as a TransitClass.
# Create the assignment class
assigclass = TransitClass(name="pt", graph=transit_graph, matrix=mat)
assig = TransitAssignment()
assig.add_class(assigclass)
# Set assignment
assig.set_time_field("trav_time")
assig.set_frequency_field("freq")
assig.set_skimming_fields(["trav_time", "boardings", "freq"])
assig.set_algorithm("os")
assigclass.set_demand_matrix_core("pt")
# Perform the assignment for the transit classes added
assig.execute()
# We can use the get_skim_results() method to retrieve the skims
assig.get_skim_results()["pt"].matrix["boardings"].sum()
898.0
Saving results#
We’ll be saving the skimming results.
assig.save_results(table_name='hyperpath example')
Wrapping up
project.close()
This project at /tmp/a29b98562aec4cf6a91c89d23f2e4f8e is already closed
Total running time of the script: (0 minutes 5.345 seconds)