""" .. _example_transit_asssignment: 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. """ # %% # .. admonition:: References # # * :doc:`../../public_transport` # %% # .. seealso:: # Several functions, methods, classes and modules are used in this example: # # * :func:`aequilibrae.transit.Transit` # * :func:`aequilibrae.transit.TransitGraphBuilder` # * :func:`aequilibrae.paths.TransitClass` # * :func:`aequilibrae.paths.TransitAssignment` # * :func:`aequilibrae.matrix.AequilibraeMatrix` # %% # 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 # sphinx_gallery_thumbnail_path = '../source/_images/transit/hyperpath_bell_n_10_alpha_100d0.png' # %% # 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 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") # %% # 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() # %% # 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()