6.5.7. aequilibrae.TrafficAssignment

class aequilibrae.TrafficAssignment

Bases: object

Traffic assignment class

For a comprehensive example on use, see the Use examples page.

from os.path import join
from aequilibrae.matrix import AequilibraeMatrix
from aequilibrae.paths import TrafficAssignment, TrafficClass


fldr = 'D:/release/Sample models/sioux_falls_2020_02_15'
proj_name = 'SiouxFalls.sqlite'
dt_fldr = '0_tntp_data'
prj_fldr = '1_project'

demand = AequilibraeMatrix()
demand.load(join(fldr, dt_fldr, 'demand.omx'))
demand.computational_view(['matrix']) # We will only assign one user class stored as 'matrix' inside the OMX file

project = Project()
project.load(join(fldr, prj_fldr))
project.network.build_graphs()

graph = project.network.graphs['c'] # we grab the graph for cars
graph.set_graph('free_flow_time') # let's say we want to minimize time
graph.set_skimming(['free_flow_time', 'distance']) # And will skim time and distance
graph.set_blocked_centroid_flows(True)

# Creates the assignment class
assigclass = TrafficClass(graph, demand)

assig = TrafficAssignment()
# The first thing to do is to add at list of traffic classes to be assigned
assig.set_classes([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 = 1000
assig.rgap_target = 0.00001

assig.execute() # we then execute the assignment

# Convergence report is here
import pandas as pd
convergence_report = pd.DataFrame(assig.assignment.convergence_report)
convergence_report.head()

# Assignment results can be viewed as a Pandas DataFrame
results_df = assig.results()

# information on the assignment setup can be recovered with
info = assig.info()

# Or save it directly to the results database
results = assig.save_results(table_name='example_from_the_documentation')

# skims are here
avg_skims = assigclass.results.skims # blended ones
last_skims = assigclass._aon_results.skims # those for the last iteration

__init__() → None

Methods

__init__()
add_class(traffic_class)	Adds a traffic class to the assignment
algorithms_available()	Returns all algorithms available for use
execute()	Processes assignment
info()	Returns information for the traffic assignment procedure
report()	Returns the assignment convergence report
results()	Prepares the assignment results as a Pandas DataFrame
save_results(table_name[, keep_zero_flows])	Saves the assignment results to results_database.sqlite
save_skims(matrix_name[, which_ones, format])	Saves the skims (if any) to the skim folder and registers in the matrix list
set_algorithm(algorithm)	Chooses the assignment algorithm.
set_capacity_field(capacity_field)	Sets the graph field that contains link capacity for the assignment period -> e.g.
set_classes(classes)	Sets Traffic classes to be assigned
set_cores(cores)	Allows one to set the number of cores to be used AFTER traffic classes have been added
set_path_file_format(file_format)	Specify path saving format.
set_save_path_files(save_it)	Turn path saving on or off.
set_time_field(time_field)	Sets the graph field that contains free flow travel time -> e.g.
set_vdf(vdf_function)	Sets the Volume-delay function to be used
set_vdf_parameters(par)	Sets the parameters for the Volume-delay function.

Attributes

all_algorithms
bpr_parameters

__init__() → None