Note
Go to the end to download the full example code
7.15. Forecasting¶
On this example we present a full forecasting workflow for the Sioux Falls example model.
## Imports
from uuid import uuid4
from tempfile import gettempdir
from os.path import join
from aequilibrae.utils.create_example import create_example
import logging
import sys
We create the example project inside our temp folder
fldr = join(gettempdir(), uuid4().hex)
project = create_example(fldr)
logger = project.logger
# We get the project open, we can tell the logger to direct all messages to the terminal as well
stdout_handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter("%(asctime)s;%(levelname)s ; %(message)s")
stdout_handler.setFormatter(formatter)
logger.addHandler(stdout_handler)
## Traffic assignment with skimming
from aequilibrae.paths import TrafficAssignment, TrafficClass
# we build all graphs
project.network.build_graphs()
# We get warnings that several fields in the project are filled with NaNs. Which is true, but we won't use those fields
2023-03-28 07:06:57,207;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2023-03-28 07:06:57,244;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2023-03-28 07:06:57,279;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2023-03-28 07:06:57,315;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2023-03-28 07:06:57,350;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
2023-03-28 07:06:57,385;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
# we grab the graph for cars
graph = project.network.graphs["c"]
# let's say we want to minimize free_flow_time
graph.set_graph("free_flow_time")
# And will skim time and distance while we are at it
graph.set_skimming(["free_flow_time", "distance"])
# And we will allow paths to be compute going through other centroids/centroid connectors
# required for the Sioux Falls network, as all nodes are centroids
graph.set_blocked_centroid_flows(False)
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:443: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[[cost_field]].reset_index()
2023-03-28 07:06:57,414;WARNING ; Cost field with wrong type. Converting to float64
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:477: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[skim_fields].reset_index()
# We get the demand matrix directly from the project record
# so let's inspect what we have in the project
proj_matrices = project.matrices
proj_matrices.list()
# Let's get it in this better way
demand = proj_matrices.get_matrix("demand_omx")
demand.computational_view(["matrix"])
assig = TrafficAssignment()
# Creates the assignment class
assigclass = TrafficClass(name="car", graph=graph, matrix=demand)
# The first thing to do is to add at list of traffic classes to be assigned
assig.add_class(assigclass)
# We set these parameters only after adding one class to the assignment
assig.set_vdf("BPR") # This is not case-sensitive # Then we set the volume delay function
assig.set_vdf_parameters({"alpha": "b", "beta": "power"}) # And its parameters
assig.set_capacity_field("capacity") # The capacity and free flow travel times as they exist in the graph
assig.set_time_field("free_flow_time")
# And the algorithm we want to use to assign
assig.set_algorithm("bfw")
# since I haven't checked the parameters file, let's make sure convergence criteria is good
assig.max_iter = 1000
assig.rgap_target = 0.001
assig.execute() # we then execute the assignment
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:443: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[[cost_field]].reset_index()
2023-03-28 07:06:57,732;WARNING ; Cost field with wrong type. Converting to float64
2023-03-28 07:06:57,733;INFO ; bfw Assignment STATS
2023-03-28 07:06:57,733;INFO ; Iteration, RelativeGap, stepsize
2023-03-28 07:06:57,738;INFO ; 1,inf,1.0
2023-03-28 07:06:57,743;INFO ; 2,0.8550751349428284,0.32839952448634563
2023-03-28 07:06:57,749;INFO ; 3,0.4763455007221067,0.18660240547488702
2023-03-28 07:06:57,755;INFO ; 4,0.2355126365951965,0.2411477440291793
2023-03-28 07:06:57,761;INFO ; 5,0.10924072010481088,0.8185470737942447
2023-03-28 07:06:57,766;INFO ; 6,0.1980945227617506,0.14054330572978305
2023-03-28 07:06:57,772;INFO ; 7,0.0668172221544687,0.36171152718899247
2023-03-28 07:06:57,778;INFO ; 8,0.06792122267870587,0.9634685345644044
2023-03-28 07:06:57,784;INFO ; 9,0.10705582933092855,0.13757153109677187
2023-03-28 07:06:57,789;INFO ; 10,0.04038814432034622,0.1609403425427973
2023-03-28 07:06:57,794;INFO ; 11,0.025801226183773084,0.716435057617116
2023-03-28 07:06:57,800;INFO ; 12,0.042846437173170424,0.08581544277016687
2023-03-28 07:06:57,805;INFO ; 13,0.016971662333407043,0.1660157969033195
2023-03-28 07:06:57,810;INFO ; 14,0.020396548012132195,0.4461322062863191
2023-03-28 07:06:57,815;INFO ; 15,0.025887901335905694,0.08515995223661561
2023-03-28 07:06:57,820;INFO ; 16,0.015188959427663162,0.1988698342670051
2023-03-28 07:06:57,826;INFO ; 17,0.01475141964322897,0.3548856159715819
2023-03-28 07:06:57,832;INFO ; 18,0.015582407302127808,0.06145415154081679
2023-03-28 07:06:57,837;INFO ; 19,0.008935871473338547,0.08603462968532699
2023-03-28 07:06:57,842;INFO ; 20,0.008477045208211683,0.1668913886047936
2023-03-28 07:06:57,848;INFO ; 21,0.009517581409988221,0.4917099156011133
2023-03-28 07:06:57,853;INFO ; 22,0.013060711845093087,0.060284308755231206
2023-03-28 07:06:57,858;INFO ; 23,0.006861821876765184,0.10954009782378307
2023-03-28 07:06:57,862;INFO ; 24,0.006201113315688483,0.12230718464290123
2023-03-28 07:06:57,867;INFO ; 25,0.0074574049738041406,0.3080614235512652
2023-03-28 07:06:57,872;INFO ; 26,0.006900497787039256,0.32835666337221175
2023-03-28 07:06:57,877;INFO ; 27,0.006963554132391016,0.7377893941135681
2023-03-28 07:06:57,883;INFO ; 28,0.006817764279834173,0.0443870768699142
2023-03-28 07:06:57,888;INFO ; 29,0.004277860366532555,0.05431813621783447
2023-03-28 07:06:57,893;INFO ; 30,0.004136181096381436,0.05758294976347482
2023-03-28 07:06:57,898;INFO ; 31,0.0031483923250298237,0.0918038853550363
2023-03-28 07:06:57,903;INFO ; 32,0.0034184967969881734,0.12279944254979965
2023-03-28 07:06:57,908;INFO ; 33,0.002738614050254322,0.08799214942487946
2023-03-28 07:06:57,913;INFO ; 34,0.0023403784016331874,0.1098259985006849
2023-03-28 07:06:57,919;INFO ; 35,0.0023185435502055523,0.18741920884713098
2023-03-28 07:06:57,925;INFO ; 36,0.0023838181828793143,0.1404967362503087
2023-03-28 07:06:57,932;INFO ; 37,0.0017801377860521138,0.25278698153070905
2023-03-28 07:06:57,937;INFO ; 38,0.0019264349761422953,0.30768123024764726
2023-03-28 07:06:57,942;INFO ; 39,0.0018408894375062524,0.3982324050247662
2023-03-28 07:06:57,947;INFO ; 40,0.0018205742523357215,0.5255149131180074
2023-03-28 07:06:57,953;INFO ; 41,0.0020224171108353135,0.012343794696331265
2023-03-28 07:06:57,957;INFO ; 42,0.001423836778473865,0.03045402621736974
2023-03-28 07:06:57,963;INFO ; 43,0.0011877471305860427,0.02283308748607117
2023-03-28 07:06:57,968;INFO ; 44,0.0012106681494599195,0.06969126002892805
2023-03-28 07:06:57,973;INFO ; 45,0.0011336232568064097,0.038970964685986896
2023-03-28 07:06:57,978;INFO ; 46,0.0009780989052684459,0.022071990851560294
2023-03-28 07:06:57,978;INFO ; bfw Assignment finished. 46 iterations and 0.0009780989052684459 final gap
# Convergence report is easy to see
import pandas as pd
convergence_report = assig.report()
convergence_report.head()
volumes = assig.results()
volumes.head()
# We could export it to CSV or AequilibraE data, but let's put it directly into the results database
assig.save_results("base_year_assignment")
# And save the skims
assig.save_skims("base_year_assignment_skims", which_ones="all", format="omx")
2023-03-28 07:06:58,035;WARNING ; Matrix Record has been saved to the database
## Trip distribution
### Calibration
# We will calibrate synthetic gravity models using the skims for TIME that we just generated
import numpy as np
from aequilibrae.distribution import GravityCalibration
# Let's take another look at what we have in terms of matrices in the model
proj_matrices.list()
# We need the demand
demand = proj_matrices.get_matrix("demand_aem")
# And the skims
imped = proj_matrices.get_matrix("base_year_assignment_skims_car")
# We can check which matrix cores were created for our skims to decide which one to use
imped.names
# Where free_flow_time_final is actually the congested time for the last iteration
['distance_blended', 'distance_final', 'free_flow_time_blended', 'free_flow_time_final']
# But before using the data, let's get some impedance for the intrazonals
# Let's assume it is 75% of the closest zone
imped_core = "free_flow_time_final"
imped.computational_view([imped_core])
# If we run the code below more than once, we will be overwriting the diagonal values with non-sensical data
# so let's zero it first
np.fill_diagonal(imped.matrix_view, 0)
# We compute it with a little bit of NumPy magic
intrazonals = np.amin(imped.matrix_view, where=imped.matrix_view > 0, initial=imped.matrix_view.max(), axis=1)
intrazonals *= 0.75
# Then we fill in the impedance matrix
np.fill_diagonal(imped.matrix_view, intrazonals)
# Since we are working with an OMX file, we cannot overwrite a matrix on disk
# So we give a new name to save it
imped.save(names=["final_time_with_intrazonals"])
# This also updates these new matrices as those being used for computation
# As one can verify below
imped.view_names
['final_time_with_intrazonals']
# We set the matrices for being used in computation
demand.computational_view(["matrix"])
for function in ["power", "expo"]:
gc = GravityCalibration(matrix=demand, impedance=imped, function=function, nan_as_zero=True)
gc.calibrate()
model = gc.model
# we save the model
model.save(join(fldr, f"{function}_model.mod"))
# We can save the result of applying the model as well
# we can also save the calibration report
with open(join(fldr, f"{function}_convergence.log"), "w") as otp:
for r in gc.report:
otp.write(r + "\n")
## Forecast
# * We create a set of * 'future' * vectors using some random growth factors
# * We apply the model for inverse power, as the TFLD seems to be a better fit for the actual one
from aequilibrae.distribution import Ipf, GravityApplication, SyntheticGravityModel
from aequilibrae.matrix import AequilibraeData
import numpy as np
# We compute the vectors from our matrix
origins = np.sum(demand.matrix_view, axis=1)
destinations = np.sum(demand.matrix_view, axis=0)
args = {
"file_path": join(fldr, "synthetic_future_vector.aed"),
"entries": demand.zones,
"field_names": ["origins", "destinations"],
"data_types": [np.float64, np.float64],
"memory_mode": False,
}
vectors = AequilibraeData()
vectors.create_empty(**args)
vectors.index[:] = demand.index[:]
# Then grow them with some random growth between 0 and 10% - Plus balance them
vectors.origins[:] = origins * (1 + np.random.rand(vectors.entries) / 10)
vectors.destinations[:] = destinations * (1 + np.random.rand(vectors.entries) / 10)
vectors.destinations *= vectors.origins.sum() / vectors.destinations.sum()
# Impedance
imped = proj_matrices.get_matrix("base_year_assignment_skims_car")
imped.computational_view(["final_time_with_intrazonals"])
# If we wanted the main diagonal to not be considered...
# np.fill_diagonal(imped.matrix_view, np.nan)
for function in ["power", "expo"]:
model = SyntheticGravityModel()
model.load(join(fldr, f"{function}_model.mod"))
outmatrix = join(proj_matrices.fldr, f"demand_{function}_model.aem")
apply = GravityApplication()
args = {
"impedance": imped,
"rows": vectors,
"row_field": "origins",
"model": model,
"columns": vectors,
"column_field": "destinations",
"nan_as_zero": True,
}
gravity = GravityApplication(**args)
gravity.apply()
# We get the output matrix and save it to OMX too,
gravity.save_to_project(name=f"demand_{function}_modeled", file_name=f"demand_{function}_modeled.omx")
2023-03-28 07:06:58,914;WARNING ; Matrix Record has been saved to the database
2023-03-28 07:06:59,063;WARNING ; Matrix Record has been saved to the database
# We update the matrices table/records and verify that the new matrices are indeed there
proj_matrices.update_database()
proj_matrices.list()
### We now run IPF for the future vectors
args = {
"matrix": demand,
"rows": vectors,
"columns": vectors,
"column_field": "destinations",
"row_field": "origins",
"nan_as_zero": True,
}
ipf = Ipf(**args)
ipf.fit()
ipf.save_to_project(name="demand_ipfd", file_name="demand_ipfd.aem")
ipf.save_to_project(name="demand_ipfd_omx", file_name="demand_ipfd.omx")
2023-03-28 07:06:59,127;WARNING ; Matrix Record has been saved to the database
2023-03-28 07:06:59,141;WARNING ; Matrix Record has been saved to the database
<aequilibrae.project.data.matrix_record.MatrixRecord object at 0x7f4808f64dc0>
proj_matrices.list()
## Future traffic assignment
from aequilibrae.paths import TrafficAssignment, TrafficClass
logger.info("\n\n\n TRAFFIC ASSIGNMENT FOR FUTURE YEAR")
2023-03-28 07:06:59,151;INFO ;
TRAFFIC ASSIGNMENT FOR FUTURE YEAR
demand = proj_matrices.get_matrix("demand_ipfd")
# let's see what is the core we ended up getting. It should be 'gravity'
demand.names
['matrix']
# Let's use the IPF matrix
demand.computational_view("matrix")
assig = TrafficAssignment()
# Creates the assignment class
assigclass = TrafficClass(name="car", graph=graph, matrix=demand)
# The first thing to do is to add at list of traffic classes to be assigned
assig.add_class(assigclass)
assig.set_vdf("BPR") # This is not case-sensitive # Then we set the volume delay function
assig.set_vdf_parameters({"alpha": "b", "beta": "power"}) # And its parameters
assig.set_capacity_field("capacity") # The capacity and free flow travel times as they exist in the graph
assig.set_time_field("free_flow_time")
# And the algorithm we want to use to assign
assig.set_algorithm("bfw")
# since I haven't checked the parameters file, let's make sure convergence criteria is good
assig.max_iter = 500
assig.rgap_target = 0.00001
# OPTIONAL: If we want to execute select link analysis on a particular TrafficClass, we set the links we are analysing
# The format of the input select links is a dictionary (str: list[tuple]).
# Each entry represents a separate set of selected links to compute. The str name will name the set of links
# The list[tuple] is the list of links being selected, of the form (link_id, direction), as it occurs in the Graph
# direction can be 0, 1, -1. 0 denotes bi-directionality
# For example, let's use Select Link on two sets of links,
select_links = {
"Leaving node 1": [(1, 1), (2, 1)],
"Random nodes": [(3, 1), (5, 1)],
}
# We call this command on the class we are analysing with our dictionary of values
assigclass.set_select_links(select_links)
assig.execute() # we then execute the assignment
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/traffic_class.py:104: UserWarning: Input string name has a space in it. Replacing with _
warnings.warn("Input string name has a space in it. Replacing with _")
/home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:443: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
df = self.__graph_groupby.sum()[[cost_field]].reset_index()
2023-03-28 07:06:59,464;WARNING ; Cost field with wrong type. Converting to float64
2023-03-28 07:06:59,464;INFO ; bfw Assignment STATS
2023-03-28 07:06:59,465;INFO ; Iteration, RelativeGap, stepsize
2023-03-28 07:06:59,472;INFO ; 1,inf,1.0
2023-03-28 07:06:59,481;INFO ; 2,0.8733395200669928,0.3176402178924753
2023-03-28 07:06:59,489;INFO ; 3,0.5191103311414231,0.18105609371890896
2023-03-28 07:06:59,496;INFO ; 4,0.28008255666275983,0.15388440268486386
2023-03-28 07:06:59,503;INFO ; 5,0.09643796551178799,0.7277343092133197
2023-03-28 07:06:59,510;INFO ; 6,0.26572325697457816,0.11409750343276846
2023-03-28 07:06:59,518;INFO ; 7,0.1125562876214825,0.1653043410096878
2023-03-28 07:06:59,524;INFO ; 8,0.08396689553370684,0.4670740574002596
2023-03-28 07:06:59,531;INFO ; 9,0.1036699197583268,0.12338795795716398
2023-03-28 07:06:59,537;INFO ; 10,0.04665684716892152,0.3164675291936488
2023-03-28 07:06:59,544;INFO ; 11,0.07232206665636076,0.12093564302721306
2023-03-28 07:06:59,551;INFO ; 12,0.0392727972260968,0.35132830832843637
2023-03-28 07:06:59,558;INFO ; 13,0.05590639696976591,0.13145288924427492
2023-03-28 07:06:59,565;INFO ; 14,0.03119131098664458,0.22364440789906556
2023-03-28 07:06:59,572;INFO ; 15,0.03583918003320254,0.1648921440226198
2023-03-28 07:06:59,580;INFO ; 16,0.023823383402599603,0.47388559103146727
2023-03-28 07:06:59,586;INFO ; 17,0.04688909355231913,0.08486818671174305
2023-03-28 07:06:59,593;INFO ; 18,0.01779350182262462,0.14165548077717843
2023-03-28 07:06:59,600;INFO ; 19,0.02104807289618521,0.2952214534723885
2023-03-28 07:06:59,606;INFO ; 20,0.02265944668806584,0.8685715069119438
2023-03-28 07:06:59,613;INFO ; 21,0.03486794637185306,0.07143141295043016
2023-03-28 07:06:59,620;INFO ; 22,0.01325716968775923,0.06111006274989798
2023-03-28 07:06:59,626;INFO ; 23,0.006888928504613831,0.07882277809507261
2023-03-28 07:06:59,632;INFO ; 24,0.007742690718402554,0.11764390919838998
2023-03-28 07:06:59,639;INFO ; 25,0.007911287116745502,0.30596683875969183
2023-03-28 07:06:59,646;INFO ; 26,0.008631010181004066,0.31811701531576797
2023-03-28 07:06:59,653;INFO ; 27,0.007283137559744894,0.5880850323340016
2023-03-28 07:06:59,661;INFO ; 28,0.007941865289218086,0.03793243235961274
2023-03-28 07:06:59,668;INFO ; 29,0.005652550997954922,0.06010604449736503
2023-03-28 07:06:59,675;INFO ; 30,0.0051420396811391565,0.08655285300393731
2023-03-28 07:06:59,682;INFO ; 31,0.0040110134882622795,0.12383871624834529
2023-03-28 07:06:59,688;INFO ; 32,0.004156292034814852,0.08120882566015596
2023-03-28 07:06:59,695;INFO ; 33,0.003386298769236586,0.12160384236591751
2023-03-28 07:06:59,702;INFO ; 34,0.0031863693702177897,0.10440603100941362
2023-03-28 07:06:59,708;INFO ; 35,0.002646179269895007,0.10441018501120305
2023-03-28 07:06:59,714;INFO ; 36,0.0027475336598291695,0.4127637406849811
2023-03-28 07:06:59,722;INFO ; 37,0.0038627879174676204,0.548277970859636
2023-03-28 07:06:59,728;INFO ; 38,0.003401148023817055,0.02186082575412126
2023-03-28 07:06:59,734;INFO ; 39,0.0021529516822476535,0.033235791355814046
2023-03-28 07:06:59,740;INFO ; 40,0.0019336552642405683,0.024954273059126474
2023-03-28 07:06:59,746;INFO ; 41,0.0017655100190603033,0.042284794993221075
2023-03-28 07:06:59,754;INFO ; 42,0.001554630255292627,0.05621437999159157
2023-03-28 07:06:59,761;INFO ; 43,0.0014167025556013288,0.0793491412541857
2023-03-28 07:06:59,768;INFO ; 44,0.0015700572447001123,0.07660991062630305
2023-03-28 07:06:59,774;INFO ; 45,0.0014952490170016294,0.21533687005810295
2023-03-28 07:06:59,780;INFO ; 46,0.0017002423731332509,0.21785716906668945
2023-03-28 07:06:59,787;INFO ; 47,0.002022643166361004,0.6597146218210108
2023-03-28 07:06:59,793;INFO ; 48,0.002458436645379931,0.011912964909181246
2023-03-28 07:06:59,801;INFO ; 49,0.0014781689293733038,0.01802700777343412
2023-03-28 07:06:59,807;INFO ; 50,0.001551305039947804,0.04705399484850306
2023-03-28 07:06:59,814;INFO ; 51,0.0010982931887360845,0.031236263736558873
2023-03-28 07:06:59,822;INFO ; 52,0.0010371219865925472,0.03209100524908013
2023-03-28 07:06:59,829;INFO ; 53,0.0008675058999404086,0.02907693389585659
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2023-03-28 07:07:01,253;INFO ; 247,2.830801973782699e-05,0.005517324194285165
2023-03-28 07:07:01,259;INFO ; 248,2.8471829222056673e-05,0.0027993025532018186
2023-03-28 07:07:01,269;INFO ; 249,2.1372047601904882e-05,0.0034493839348449643
2023-03-28 07:07:01,276;INFO ; 250,1.9613528369563637e-05,0.0014973388442102381
2023-03-28 07:07:01,283;INFO ; 251,1.6410277807400067e-05,0.004003578470088645
2023-03-28 07:07:01,289;INFO ; 252,1.6528840158950062e-05,0.0019469072754988747
2023-03-28 07:07:01,296;INFO ; 253,1.731558262761762e-05,0.00511179593752691
2023-03-28 07:07:01,303;INFO ; 254,1.9617219611477338e-05,0.0032432982614396385
2023-03-28 07:07:01,309;INFO ; 255,1.789002435439406e-05,0.005625207907716573
2023-03-28 07:07:01,316;INFO ; 256,2.3415394511240857e-05,0.006561163775871654
2023-03-28 07:07:01,322;INFO ; 257,1.9064596408601253e-05,0.0035705009525325224
2023-03-28 07:07:01,329;INFO ; 258,2.363895019163747e-05,0.007559225761155003
2023-03-28 07:07:01,335;INFO ; 259,2.509874891018198e-05,0.006413278091965013
2023-03-28 07:07:01,342;INFO ; 260,2.4574141974743037e-05,0.005618618743644352
2023-03-28 07:07:01,348;INFO ; 261,2.1806843331136008e-05,0.004331681370838184
2023-03-28 07:07:01,355;INFO ; 262,2.0611592473898758e-05,0.00595640355378514
2023-03-28 07:07:01,362;INFO ; 263,2.3163733967289257e-05,0.007882346485960283
2023-03-28 07:07:01,368;INFO ; 264,2.6309472349138578e-05,0.008920189103632197
2023-03-28 07:07:01,374;INFO ; 265,2.0387672255912822e-05,0.005943874879461277
2023-03-28 07:07:01,381;INFO ; 266,2.1530592298567718e-05,0.007179746172119635
2023-03-28 07:07:01,387;INFO ; 267,1.972919489796362e-05,0.00679929743681663
2023-03-28 07:07:01,395;INFO ; 268,2.5735155080485153e-05,0.008350467237359166
2023-03-28 07:07:01,401;INFO ; 269,2.7566843276653174e-05,0.007705720490948829
2023-03-28 07:07:01,408;INFO ; 270,2.300320294449574e-05,0.007865243677607311
2023-03-28 07:07:01,414;INFO ; 271,2.125314410458199e-05,0.0042720518357252215
2023-03-28 07:07:01,421;INFO ; 272,2.0043053076406044e-05,0.0035380063776877975
2023-03-28 07:07:01,430;INFO ; 273,1.909625233344795e-05,0.0033239525468961844
2023-03-28 07:07:01,437;INFO ; 274,1.8243192576324124e-05,0.00398739845642326
2023-03-28 07:07:01,443;INFO ; 275,2.043413005298623e-05,0.005970447834328287
2023-03-28 07:07:01,450;INFO ; 276,1.740583013072514e-05,0.0028418733443022126
2023-03-28 07:07:01,457;INFO ; 277,1.787678572883007e-05,0.0028666722071158408
2023-03-28 07:07:01,464;INFO ; 278,1.6072770861548098e-05,0.0017795401923408314
2023-03-28 07:07:01,470;INFO ; 279,1.3002193142257764e-05,0.0013782843717829777
2023-03-28 07:07:01,477;INFO ; 280,1.2137070498327178e-05,0.0012397345109950938
2023-03-28 07:07:01,483;INFO ; 281,9.837246751073227e-06,0.0008434274035069354
2023-03-28 07:07:01,483;INFO ; bfw Assignment finished. 281 iterations and 9.837246751073227e-06 final gap
Now let us save our select link results, all we need to do is provide it with a name In additional to exporting the select link flows, it also exports the Select Link matrices in OMX format.
assig.save_select_link_results("select_link_analysis")
Say we just want to save our select link flows, we can call:
assig.save_select_link_flows("just_flows")
# Or if we just want the SL matrices:
assig.save_select_link_matrices("just_matrices")
# Internally, the save_select_link_results calls both of these methods at once.
# We could export it to CSV or AequilibraE data, but let's put it directly into the results database
assig.save_results("future_year_assignment")
# And save the skims
assig.save_skims("future_year_assignment_skims", which_ones="all", format="omx")
2023-03-28 07:07:01,549;WARNING ; Matrix Record has been saved to the database
We can also plot convergence
import matplotlib.pyplot as plt
df = assig.report()
x = df.iteration.values
y = df.rgap.values
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(x, y, "k--")
plt.yscale("log")
plt.grid(True, which="both")
plt.xlabel(r"Iterations")
plt.ylabel(r"Relative Gap")
plt.show()
Close the project
project.close()
Total running time of the script: ( 0 minutes 4.780 seconds)