Note
Go to the end to download the full example code.
Public transport assignment with Optimal Strategies#
In this example, we import a GTFS feed to our model, create a public transport network, create project match connectors, and perform a Spiess & Florian assignment. Click here to check out the article.
We use data from Coquimbo, a city in La Serena Metropolitan Area in Chile.
References
See also
Several functions, methods, classes and modules are used in this example:
# Imports for example construction
from uuid import uuid4
from os.path import join
from tempfile import gettempdir
from aequilibrae.transit import Transit
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")
# We drop geometry here for the sake of display.
graph.vertices.drop(columns="geometry")
graph.edges
The graphs also also stored in the Transit.graphs dictionary. They are keyed by the ‘period_id’ they
were created for. A graph for a different ‘period_id’ can be created by providing period_id= in the
Transit.create_graph call. You can view previously created periods with the Periods object.
periods = project.network.periods
periods.data
Connector project matching#
project.network.build_graphs()
Now we’ll create the line strings for the access connectors, this step is optinal but provides more accurate distance estimations and better looking geometry.
Because Coquimbo doesn’t have many walking edges we’ll match onto the "c" graph.
graph.create_line_geometry(method="connector project match", graph="c")
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/aequilibrae/transit/transit_graph_builder.py:1215: UserWarning: In its current implementation, the "connector project match" method may take a while for large networks.
warnings.warn(
Saving and reloading#
Lets save all graphs to the ‘public_transport.sqlite’ database.
data.save_graphs()
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/aequilibrae/transit/transit.py:99: UserWarning: Currently only a single transit graph can be saved and reloaded. Multiple graph support is plan for a future release.
warnings.warn(
We can reload the saved graphs with data.load.
This will create new TransitGraphBuilder's based on the ‘period_id’ of the saved graphs.
The graph configuration is stored in the ‘transit_graph_config’ table in ‘project_database.sqlite’
as serialised JSON.
data.load()
/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/aequilibrae/transit/transit.py:113: UserWarning: Currently only a single transit graph can be saved and reloaded. Multiple graph support is plan for a future release. `period_ids` argument is currently ignored.
warnings.warn(
Links and nodes are stored in a similar manner to the ‘project_database.sqlite’ database.
Reading back into AequilibraE#
You can create back in a particular graph via it’s ‘period_id’.
from aequilibrae.project.database_connection import database_connection
from aequilibrae.transit.transit_graph_builder import TransitGraphBuilder
pt_con = database_connection("transit")
graph_db = TransitGraphBuilder.from_db(pt_con, periods.default_period.period_id)
graph_db.vertices.drop(columns="geometry")
graph_db.edges
Converting to a AequilibraE graph object#
To perform an assignment we need to convert the graph builder into a graph.
transit_graph = graph.to_transit_graph()
Mock demand matrix#
We’ll create a mock demand matrix with demand 1 for every zone.
We’ll also need to convert from zone_id's to node_id's.
import numpy as np
from aequilibrae.matrix import AequilibraeMatrix
zones_in_the_model = len(transit_graph.centroids)
names_list = ['pt']
mat = AequilibraeMatrix()
mat.create_empty(zones=zones_in_the_model,
matrix_names=names_list,
memory_only=True)
mat.index = transit_graph.centroids[:]
mat.matrices[:, :, 0] = np.full((zones_in_the_model, zones_in_the_model), 1.0)
mat.computational_view()
Hyperpath generation/assignment#
We’ll create a TransitAssignment object as well as a TransitClass
from aequilibrae.paths import TransitAssignment, TransitClass
# Create the assignment class
assigclass = TransitClass(name="pt", graph=transit_graph, matrix=mat)
assig = TransitAssignment()
assig.add_class(assigclass)
# We need to tell AequilbraE where to find the appropriate fields we want to use,
# as well as the assignment algorithm to use.
assig.set_time_field("trav_time")
assig.set_frequency_field("freq")
assig.set_algorithm("os")
# When there's multiple matrix cores we'll also need to set the core to use for the demand.
assigclass.set_demand_matrix_core("pt")
# Let's perform the assignment for the transit classes added
assig.execute()
View the results
assig.results()
Saving results#
We’ll be saving the results to another sqlite db called ‘results_database.sqlite’. The ‘results’ table with ‘project_database.sqlite’ contains some metadata about each table in ‘results_database.sqlite’.
assig.save_results(table_name='hyperpath example')
Wrapping up
project.close()
Total running time of the script: (0 minutes 4.869 seconds)