9.5.7. 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, proj_name))
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()

# Link flow results are here
results = assigclass.results

# skims are here
avg_skims = assigclass.results.skims # blended ones
last_skims = assigclass._aon_results.skims # those for the last iteration
__init__() → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__()

Initialize self.

algorithms_available()

Returns all algorithms available for use

execute()

Processes assignment

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_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

bpr_parameters = ['alpha', 'beta']
all_algorithms = ['all-or-nothing', 'msa', 'frank-wolfe', 'cfw', 'bfw']
__init__() → None

Initialize self. See help(type(self)) for accurate signature.

set_vdf(vdf_function: str) → None

Sets the Volume-delay function to be used

Args:

vdf_function(str:) Name of the VDF to be used

set_classes(classes: List[aequilibrae.paths.traffic_class.TrafficClass]) → None

Sets Traffic classes to be assigned

Args:

classes(List[TrafficClass]:) List of Traffic classes for assignment

algorithms_available() → list

Returns all algorithms available for use

Returns:

list: List of string values to be used with set_algorithm

set_algorithm(algorithm: str)

Chooses the assignment algorithm. e.g. ‘frank-wolfe’, ‘bfw’, ‘msa’

Args:

algorithm (list): Algorithm to be used

set_vdf_parameters(par: dict) → None

Sets the parameters for the Volume-delay function.

Parameter values can be scalars (same values for the entire network) or network field names (link-specific values) - Examples: {‘alpha’: 0.15, ‘beta’: 4.0} or {‘alpha’: ‘alpha’, ‘beta’: ‘beta’}

Args:

par (dict): Dictionary with all parameters for the chosen VDF

set_cores(cores: int) → None

Allows one to set the number of cores to be used AFTER traffic classes have been added

Inherited from AssignmentResults

Args:

cores (int): Number of CPU cores to use

set_time_field(time_field: str) → None

Sets the graph field that contains free flow travel time -> e.g. ‘fftime’

Args:

time_field (str): Field name

set_capacity_field(capacity_field: str) → None

Sets the graph field that contains link capacity for the assignment period -> e.g. ‘capacity1h’

Args:

capacity_field (str): Field name

execute() → None

Processes assignment

_TrafficAssignment__check_attributes(instance, value)
_TrafficAssignment__collect_data()
_TrafficAssignment__validate_parameters(kwargs) → bool