aequilibrae.paths.TransitAssignment

class aequilibrae.paths.TransitAssignment(*args, project=None, **kwargs)

__init__(*args, project=None, **kwargs)

Methods

__init__(*args[, project])
add_class(transport_class)	Adds a Transport class to the assignment
algorithms_available()	Returns all algorithms available for use
execute([log_specification])	Processes assignment
info()	Returns information for the transit 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
set_algorithm(algorithm)	Chooses the assignment algorithm.
set_classes(classes)	Sets Transport classes to be assigned
set_cores(cores)	Allows one to set the number of cores to be used AFTER transit classes have been added
set_frequency_field(frequency_field)	Sets the graph field that contains the frequency -> e.g. 'freq'.
set_time_field(time_field)	Sets the graph field that contains free flow travel time -> e.g. 'trav_time'.

Attributes

all_algorithms

add_class(transport_class: TransportClassBase) → None

Adds a Transport class to the assignment

Arguments:

transport_class (TransportClassBase): Transport class

algorithms_available() → list

Returns all algorithms available for use

Returns:

list: List of string values to be used with set_algorithm

execute(log_specification=True) → None

Processes assignment

info() → dict

Returns information for the transit assignment procedure

Dictionary contains keys ‘Algorithm’, ‘Classes’, ‘Computer name’, ‘Procedure ID’.

The classes key is also a dictionary with all the user classes per transit class and their respective matrix totals

Returns:

info (dict): Dictionary with summary information

log_specification()

report() → DataFrame

Returns the assignment convergence report

Returns:

DataFrame (pd.DataFrame): Convergence report

results() → DataFrame

Prepares the assignment results as a Pandas DataFrame

Returns:

DataFrame (pd.DataFrame): Pandas DataFrame with all the assignment results indexed on link_id

save_results(table_name: str, keep_zero_flows=True, project=None) → None

Saves the assignment results to results_database.sqlite

Method fails if table exists

Arguments:

table_name (str): Name of the table to hold this assignment result

keep_zero_flows (bool): Whether we should keep records for zero flows. Defaults to True

project (Project, Optional): Project we want to save the results to. Defaults to the active project

set_algorithm(algorithm: str)

Chooses the assignment algorithm. Currently only ‘optimal-strategies’ is available.

‘os’ is also accepted as an alternative to ‘optimal-strategies’

Arguments:

algorithm (str): Algorithm to be used

set_classes(classes: List[TransportClassBase]) → None

Sets Transport classes to be assigned

Arguments:

classes (List[TransportClassBase]): List of TransportClass’s for assignment

set_cores(cores: int) → None

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

Inherited from AssignmentResultsBase

Arguments:

cores (int): Number of CPU cores to use

set_frequency_field(frequency_field: str) → None

Sets the graph field that contains the frequency -> e.g. ‘freq’

Arguments:

frequency_field (str): Field name

set_time_field(time_field: str) → None

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

Arguments:

time_field (str): Field name

all_algorithms = ['optimal-strategies', 'os']