aequilibrae.paths.TransitAssignment#

class aequilibrae.paths.TransitAssignment(*args, project=None, **kwargs)[source]#
__init__(*args, project=None, **kwargs)[source]#

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

get_skim_results()

Prepares the assignment skim results for all classes

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_skimming_fields([skimming_fields])

Sets the skimming fields for the transit assignment.

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

get_skim_results() → list#

Prepares the assignment skim results for all classes

Returns:

skim list (list): Lists of all skims with the results for each class

info() → dict[source]#

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()[source]#
report() → DataFrame#

Returns the assignment convergence report

Returns:

DataFrame (pd.DataFrame): Convergence report

results() → DataFrame[source]#

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[source]#

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)[source]#

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[source]#

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[source]#

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

Arguments:

frequency_field (str): Field name

set_skimming_fields(skimming_fields: list[str] | None = None) → None[source]#

Sets the skimming fields for the transit assignment.

Also accepts predefined skimming fields:

  • discrete: ‘boardings’, ‘alightings’, ‘inner_transfers’, ‘outer_transfers’, and ‘transfers’.

  • continuous: ‘trav_time’, ‘on_board_trav_time’, ‘dwelling_time’, ‘egress_trav_time’, ‘access_trav_time’, ‘walking_trav_time’, ‘transfer_time’, ‘in_vehicle_trav_time’, and ‘waiting_time’.

Provide no argument to disable.

Arguments:

skimming_fields (list[str]): Optional list of field names, or predefined skimming type.

set_time_field(time_field: str) → None[source]#

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']#