Note
Go to the end to download the full example code.
Public transport assignment with Optimal Strategies#
In this example, perform a Spiess & Florian assignment. Click here to check out the paper.
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()
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
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55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
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91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
127 128 129 130 131 132 133]
warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
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37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
127 128 129 130 131 132 133]
warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))
Now we’ll create the line strings for the access connectors, this step is optional 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")
/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(
Saving and reloading#
Lets save all graphs to the ‘public_transport.sqlite’ database.
data.save_graphs()
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()
We can also remove the previously saved graphs.
# data.remove_graphs()
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()
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
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46 51 52 53 54 55 56 61 62 63 64 65 66 67 72 73 77 78
79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
98 99 100 101 102 103 104 105 109 110 111 114 115 116 117 118 119 120
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]))
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
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46 51 52 53 54 55 56 61 62 63 64 65 66 67 72 73 77 78
79 80 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97
98 99 100 101 102 103 104 105 109 110 111 114 115 116 117 118 119 120
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#
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 as we can only assign one at a time.
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()
This project at /tmp/2bca3e48d98f45d59c57b345fb5d5f55 is already closed
Total running time of the script: (0 minutes 5.468 seconds)