.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "route_choice/_auto_examples/plot_route_choice_basics.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_route_choice__auto_examples_plot_route_choice_basics.py: .. _example_usage_route_choice: Route Choice ============ In this example, we show how to perform route choice set generation using BFSLE and Link penalisation, for a city in La Serena Metropolitan Area in Chile. .. GENERATED FROM PYTHON SOURCE LINES 11-14 .. admonition:: References * :doc:`../../route_choice` .. GENERATED FROM PYTHON SOURCE LINES 16-22 .. seealso:: Several functions, methods, classes and modules are used in this example: * :func:`aequilibrae.paths.Graph` * :func:`aequilibrae.paths.RouteChoice` * :func:`aequilibrae.matrix.AequilibraeMatrix` .. GENERATED FROM PYTHON SOURCE LINES 24-32 .. code-block:: Python # Imports from uuid import uuid4 from tempfile import gettempdir from os.path import join from aequilibrae.utils.create_example import create_example .. GENERATED FROM PYTHON SOURCE LINES 34-40 .. code-block:: Python # We create the example project inside our temp folder fldr = join(gettempdir(), uuid4().hex) project = create_example(fldr, "coquimbo") .. GENERATED FROM PYTHON SOURCE LINES 41-44 .. code-block:: Python import logging import sys .. GENERATED FROM PYTHON SOURCE LINES 45-53 .. code-block:: Python # When the project opens, we can tell the logger to direct all messages to the terminal as well logger = project.logger stdout_handler = logging.StreamHandler(sys.stdout) formatter = logging.Formatter("%(asctime)s;%(levelname)s ; %(message)s") stdout_handler.setFormatter(formatter) logger.addHandler(stdout_handler) .. GENERATED FROM PYTHON SOURCE LINES 54-56 Model parameters ---------------- .. GENERATED FROM PYTHON SOURCE LINES 56-59 .. code-block:: Python import numpy as np .. GENERATED FROM PYTHON SOURCE LINES 60-63 We'll set the parameters for our route choice model. These are the parameters that will be used to calculate the utility of each path. In our example, the utility is equal to :math:`distance * theta`, and the path overlap factor (PSL) is equal to :math:`beta`. .. GENERATED FROM PYTHON SOURCE LINES 63-70 .. code-block:: Python # Distance factor theta = 0.00011 # PSL parameter beta = 1.1 .. GENERATED FROM PYTHON SOURCE LINES 71-72 Let's select a set of nodes of interest .. GENERATED FROM PYTHON SOURCE LINES 72-74 .. code-block:: Python nodes_of_interest = (71645, 74089, 77011, 79385) .. GENERATED FROM PYTHON SOURCE LINES 75-76 Let's build all graphs .. GENERATED FROM PYTHON SOURCE LINES 76-80 .. code-block:: Python project.network.build_graphs() # We get warnings that several fields in the project are filled with NaNs. # This is true, but we won't use those fields. .. rst-class:: sphx-glr-script-out .. code-block:: none 2025-01-15 19:56:32,433;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations 2025-01-15 19:56:32,513;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations 2025-01-15 19:56:32,614;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations 2025-01-15 19:56:32,716;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations .. GENERATED FROM PYTHON SOURCE LINES 81-82 We also see what graphs are available .. GENERATED FROM PYTHON SOURCE LINES 82-84 .. code-block:: Python project.network.graphs.keys() .. rst-class:: sphx-glr-script-out .. code-block:: none dict_keys(['b', 'c', 't', 'w']) .. GENERATED FROM PYTHON SOURCE LINES 85-86 We grab the graph for cars .. GENERATED FROM PYTHON SOURCE LINES 86-102 .. code-block:: Python graph = project.network.graphs["c"] # Let's say that utility is just a function of distance, so we build our 'utility' field as distance * theta graph.network = graph.network.assign(utility=graph.network.distance * theta) # Prepare the graph with all nodes of interest as centroids graph.prepare_graph(np.array(nodes_of_interest)) # And set the cost of the graph the as the utility field just created graph.set_graph("utility") # We allow flows through "centroid connectors" because our centroids are not really centroids # If we have actual centroid connectors in the network (and more than one per centroid), then we # should remove them from the graph graph.set_blocked_centroid_flows(False) .. rst-class:: sphx-glr-script-out .. code-block:: none 2025-01-15 19:56:32,794;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations .. GENERATED FROM PYTHON SOURCE LINES 103-106 Mock demand matrix ------------------ We'll create a mock demand matrix with demand 1 for every zone and prepare it for computation. .. GENERATED FROM PYTHON SOURCE LINES 106-117 .. code-block:: Python from aequilibrae.matrix import AequilibraeMatrix names_list = ["demand", "5x demand"] mat = AequilibraeMatrix() mat.create_empty(zones=graph.num_zones, matrix_names=names_list, memory_only=True) mat.index = graph.centroids[:] mat.matrices[:, :, 0] = np.full((graph.num_zones, graph.num_zones), 10.0) mat.matrices[:, :, 1] = np.full((graph.num_zones, graph.num_zones), 50.0) mat.computational_view() .. GENERATED FROM PYTHON SOURCE LINES 118-121 Create plot function -------------------- Before dive into the Route Choice class, let's define a function to plot assignment results. .. GENERATED FROM PYTHON SOURCE LINES 121-123 .. code-block:: Python import folium .. GENERATED FROM PYTHON SOURCE LINES 124-155 .. code-block:: Python def plot_results(link_loads): link_loads = link_loads[link_loads.tot > 0] max_load = link_loads["tot"].max() links = project.network.links.data loaded_links = links.merge(link_loads, on="link_id", how="inner") loads_lyr = folium.FeatureGroup("link_loads") # Maximum thickness we would like is probably a 10, so let's make sure we don't go over that factor = 10 / max_load # Let's create the layers for _, rec in loaded_links.iterrows(): points = rec.geometry.wkt.replace("LINESTRING ", "").replace("(", "").replace(")", "").split(", ") points = "[[" + "],[".join([p.replace(" ", ", ") for p in points]) + "]]" # we need to take from x/y to lat/long points = [[x[1], x[0]] for x in eval(points)] _ = folium.vector_layers.PolyLine( points, tooltip=f"link_id: {rec.link_id}, Flow: {rec.tot:.3f}", color="red", weight=factor * rec.tot, ).add_to(loads_lyr) long, lat = project.conn.execute("select avg(xmin), avg(ymin) from idx_links_geometry").fetchone() map_osm = folium.Map(location=[lat, long], tiles="Cartodb Positron", zoom_start=12) loads_lyr.add_to(map_osm) folium.LayerControl().add_to(map_osm) return map_osm .. GENERATED FROM PYTHON SOURCE LINES 156-159 Route Choice class ------------------ Here we'll construct and use the Route Choice class to generate our route sets .. GENERATED FROM PYTHON SOURCE LINES 159-161 .. code-block:: Python from aequilibrae.paths import RouteChoice .. GENERATED FROM PYTHON SOURCE LINES 162-164 This object construct might take a minute depending on the size of the graph due to the construction of the compressed link to network link mapping that's required. This is a one time operation per graph and is cached. .. GENERATED FROM PYTHON SOURCE LINES 164-169 .. code-block:: Python rc = RouteChoice(graph) # Let's check the default parameters for the Route Choice class print(rc.default_parameters) .. rst-class:: sphx-glr-script-out .. code-block:: none {'generic': {'seed': 0, 'max_routes': 0, 'max_depth': 0, 'max_misses': 100, 'penalty': 1.01, 'cutoff_prob': 0.0, 'beta': 1.0, 'store_results': True}, 'link-penalisation': {}, 'bfsle': {'penalty': 1.0}} .. GENERATED FROM PYTHON SOURCE LINES 170-171 Let's add the demand. If it's not provided, link loading cannot be preformed. .. GENERATED FROM PYTHON SOURCE LINES 171-173 .. code-block:: Python rc.add_demand(mat) .. GENERATED FROM PYTHON SOURCE LINES 174-175 It is highly recommended to set either ``max_routes`` or ``max_depth`` to prevent runaway results. .. GENERATED FROM PYTHON SOURCE LINES 175-177 .. code-block:: Python rc.set_choice_set_generation("bfsle", max_routes=5) .. GENERATED FROM PYTHON SOURCE LINES 178-180 We can now perform a computation for single OD pair if we'd like. Here we do one between the first and last centroid as well as an assignment. .. GENERATED FROM PYTHON SOURCE LINES 180-183 .. code-block:: Python results = rc.execute_single(77011, 74089, demand=1.0) print(results[0]) .. rst-class:: sphx-glr-script-out .. code-block:: none (24222, 30332, 30333, 10435, 30068, 30069, 14198, 14199, 31161, 30928, 30929, 30930, 30931, 24172, 30878, 30879, 30880, 30881, 30882, 30883, 30884, 30885, 30886, 30887, 30888, 30889, 30890, 30891, 5179, 5180, 5181, 5182, 26463, 26462, 26461, 26460, 26459, 26458, 26457, 26456, 26480, 3341, 3342, 3339, 9509, 9510, 9511, 9512, 18487, 14972, 14973, 32692, 32693, 32694, 2300, 2301, 33715, 19978, 19979, 19977, 19976, 19975, 19974, 19973, 19972, 19971, 19970, 22082, 22080, 5351, 5352, 2280, 2281, 2282, 575, 576, 577, 578, 579, 536, 537, 538, 539, 540, 541, 15406, 15407, 15408, 553, 552, 633, 634, 635, 630, 631, 632, 623, 624, 625, 626, 471, 5363, 34169, 34170, 34171, 34785, 6466, 6465, 29938, 29939, 29940, 29941, 1446, 1447, 1448, 1449, 1450, 939, 940, 941, 9840, 9841, 26314, 26313, 26312, 26311, 26310, 26309, 26308, 26307, 26306, 26305, 26304, 26303, 26302, 26301, 26300, 34079, 34147, 29962, 26422, 26421, 26420, 765, 764, 763, 762, 761, 760, 736, 10973, 10974, 10975, 725, 10972, 727, 728, 26424, 733, 734, 29899, 20970, 20969, 20968, 20967, 20966, 20965, 20964, 20963, 20962, 9584, 9583, 20981, 21398, 20982, 34208, 35, 36, 59, 60, 61, 22363, 22364, 22365, 22366, 22367, 28958, 28959, 28960, 28961, 28962, 28805, 28806, 28807, 28808, 28809, 28810, 28827, 28828, 28829, 28830, 28874) .. GENERATED FROM PYTHON SOURCE LINES 184-186 Because we asked it to also perform an assignment we can access the various results from that. The default return is a Pyarrow Table but Pandas is nicer for viewing. .. GENERATED FROM PYTHON SOURCE LINES 186-189 .. code-block:: Python res = rc.get_results().to_pandas() res.head() .. raw:: html
origin id destination id route set cost mask path overlap probability
0 77011 74089 [24222, 30332, 30333, 10435, 30068, 30069, 141... 1.907420 True 0.386507 0.231745
1 77011 74089 [24222, 30332, 30333, 10435, 30068, 30069, 141... 1.890617 True 0.287132 0.175078
2 77011 74089 [24223, 31635, 31636, 31637, 31638, 31639, 316... 1.896528 True 0.304541 0.184599
3 77011 74089 [24222, 30332, 30333, 10435, 30068, 30069, 141... 1.909910 True 0.430542 0.257506
4 77011 74089 [24222, 30332, 30333, 10435, 30068, 30069, 141... 1.886882 True 0.246836 0.151071


.. GENERATED FROM PYTHON SOURCE LINES 190-192 .. code-block:: Python plot_results(rc.get_load_results()["demand"]) .. raw:: html
Make this Notebook Trusted to load map: File -> Trust Notebook


.. GENERATED FROM PYTHON SOURCE LINES 193-197 Batch operations ---------------- To perform a batch operation we need to prepare the object first. We can either provide a list of tuple of the OD pairs we'd like to use, or we can provided a 1D list and the generation will be run on all permutations. .. GENERATED FROM PYTHON SOURCE LINES 197-199 .. code-block:: Python rc.prepare() .. GENERATED FROM PYTHON SOURCE LINES 200-201 Now we can perform a batch computation with an assignment .. GENERATED FROM PYTHON SOURCE LINES 201-205 .. code-block:: Python rc.execute(perform_assignment=True) res = rc.get_results().to_pandas() res.head() .. raw:: html
origin id destination id route set cost mask path overlap probability
0 71645 74089 [19550, 19549, 19548, 19547, 19546, 19545, 209... 0.607017 True 0.384514 0.204478
1 71645 74089 [19550, 19549, 19548, 19547, 19546, 19545, 195... 0.607425 True 0.273951 0.145623
2 71645 74089 [19550, 19549, 19548, 19547, 19546, 19545, 209... 0.622079 True 0.544362 0.285155
3 71645 74089 [19550, 19549, 19548, 19547, 19546, 19545, 209... 0.611944 True 0.443492 0.234682
4 71645 74089 [19550, 19549, 19548, 19547, 19546, 19545, 209... 0.604384 True 0.243935 0.130063


.. GENERATED FROM PYTHON SOURCE LINES 206-207 Since we provided a matrix initially we can also perform link loading based on our assignment results. .. GENERATED FROM PYTHON SOURCE LINES 207-209 .. code-block:: Python rc.get_load_results() .. raw:: html
demand 5x demand demand 5x demand
ab ba ab ba tot tot
link_id
1 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0
12 0.0 0.0 0.0 0.0 0.0 0.0
13 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ...
34938 0.0 0.0 0.0 0.0 0.0 0.0
34939 0.0 0.0 0.0 0.0 0.0 0.0
34940 0.0 0.0 0.0 0.0 0.0 0.0
34941 0.0 0.0 0.0 0.0 0.0 0.0
34942 0.0 0.0 0.0 0.0 0.0 0.0

19983 rows × 6 columns



.. GENERATED FROM PYTHON SOURCE LINES 210-211 We can plot these as well .. GENERATED FROM PYTHON SOURCE LINES 211-213 .. code-block:: Python plot_results(rc.get_load_results()["demand"]) .. raw:: html
Make this Notebook Trusted to load map: File -> Trust Notebook


.. GENERATED FROM PYTHON SOURCE LINES 214-219 Select link analysis -------------------- We can also enable select link analysis by providing the links and the directions that we are interested in. Here we set the select link to trigger when (7369, 1) and (20983, 1) is utilised in "sl1" and "sl2" when (7369, 1) is utilised. .. GENERATED FROM PYTHON SOURCE LINES 219-222 .. code-block:: Python rc.set_select_links({"sl1": [[(7369, 1), (20983, 1)]], "sl2": [[(7369, 1)]]}) rc.execute(perform_assignment=True) .. GENERATED FROM PYTHON SOURCE LINES 223-224 We can get then the results in a Pandas DataFrame for both the network. .. GENERATED FROM PYTHON SOURCE LINES 224-227 .. code-block:: Python sl = rc.get_select_link_loading_results() sl .. raw:: html
demand 5x demand demand 5x demand demand 5x demand demand 5x demand
sl1 sl1 sl2 sl2 sl1 sl1 sl2 sl2
ab ba ab ba ab ba ab ba tot tot tot tot
link_id
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ...
34938 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
34939 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
34940 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
34941 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
34942 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

19983 rows × 12 columns



.. GENERATED FROM PYTHON SOURCE LINES 228-231 We can also access the OD matrices for this link loading. These matrices are sparse and can be converted to SciPy sparse matrices for ease of use. They're stored in a dictionary where the key is the matrix name concatenated with the select link set name via an underscore. .. GENERATED FROM PYTHON SOURCE LINES 231-233 .. code-block:: Python rc.get_select_link_od_matrix_results() .. rst-class:: sphx-glr-script-out .. code-block:: none {'sl1': {'demand': , '5x demand': }, 'sl2': {'demand': , '5x demand': }} .. GENERATED FROM PYTHON SOURCE LINES 234-237 .. code-block:: Python od_matrix = rc.get_select_link_od_matrix_results()["sl1"]["demand"] od_matrix.to_scipy().toarray() .. rst-class:: sphx-glr-script-out .. code-block:: none array([[0. , 0. , 0. , 3.04610785], [0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. ]]) .. GENERATED FROM PYTHON SOURCE LINES 238-239 .. code-block:: Python project.close() .. rst-class:: sphx-glr-script-out .. code-block:: none INFO:aequilibrae:Closed project on /tmp/f8d5a03243d548bd88e74fb3849d550c .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 4.286 seconds) .. _sphx_glr_download_route_choice__auto_examples_plot_route_choice_basics.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_route_choice_basics.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_route_choice_basics.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_route_choice_basics.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_