aequilibrae.paths.TrafficAssignment

class aequilibrae.paths.TrafficAssignment(project=None)

Traffic assignment class.

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

>>> from aequilibrae import Project
>>> from aequilibrae.matrix import AequilibraeMatrix
>>> from aequilibrae.paths import TrafficAssignment, TrafficClass

>>> project = Project.from_path("/tmp/test_project")
>>> 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)

>>> proj_matrices = project.matrices

>>> demand = AequilibraeMatrix()
>>> demand = proj_matrices.get_matrix("demand_omx")

# We will only assign one user class stored as 'matrix' inside the OMX file
>>> demand.computational_view(['matrix'])

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

>>> assig = TrafficAssignment()

# The first thing to do is to add at list of traffic classes to be assigned
>>> assig.set_classes([assigclass])

# Then we set the volume delay function
>>> assig.set_vdf("BPR")  # This is not case-sensitive

# And its parameters
>>> assig.set_vdf_parameters({"alpha": "b", "beta": "power"})

# The capacity and free flow travel times as they exist in the graph
>>> assig.set_capacity_field("capacity")
>>> assig.set_time_field("free_flow_time")

# And the algorithm we want to use to assign
>>> assig.set_algorithm('bfw')

# Since we 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

# If you want, it is possible to access the convergence report
>>> import pandas as pd
>>> convergence_report = pd.DataFrame(assig.assignment.convergence_report)

# 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='base_year_assignment')

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

__init__(project=None) → None

Methods

__init__([project])
add_class(traffic_class)	Adds a traffic class to the assignment
algorithms_available()	Returns all algorithms available for use
execute([log_specification])	Processes assignment
info()	Returns information for the traffic assignment procedure
log_specification()
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_select_link_flows(table_name[, project])	Saves the select link link flows for all classes into the results database.
save_select_link_matrices(file_name)	Saves the Select Link matrices for each TrafficClass in the current TrafficAssignment class
save_select_link_results(name)	Saves both the Select Link matrices and flow results at the same time, using the same name.
save_skims(matrix_name[, which_ones, ...])	Saves the skims (if any) to the skim folder and registers in the matrix list
select_link_flows()	Returns a dataframe of the select link flows for each class
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. 'capacity1h'.
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. 'fftime'.
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