traffic_kpi

coefficients_tape

class CoefficientDefinition(coefficient_name: str, share_name: str, load_capacity: str | None = None, effective_load_factor: str | None = None)

Bases: object

coefficient_name: str
effective_load_factor: str | None = None
load_capacity: str | None = None
share_name: str
class CoefficientsTape

Bases: CsvTape

add_coefficient(category: str, kpi: str, *coeffiencent_names)
coefficient_names: Dict[Tuple[str, str], List[Tuple[str, ...]]]
coefficients: Dict
get_data(key: Tuple[str, str]) → List[ndarray]
initialize(csv: DataFrame, time_column: str = 'seconds')

entities

class TransportSegments(name: str = None)

Bases: EntityGroup

attributes: Dict[str, AttributeField] = {'cargo_flow': <movici_simulation_core.core.attribute.AttributeField object>, 'co2_emission': <movici_simulation_core.core.attribute.AttributeField object>, 'energy_consumption': <movici_simulation_core.core.attribute.AttributeField object>, 'length': <movici_simulation_core.core.attribute.AttributeField object>, 'nox_emission': <movici_simulation_core.core.attribute.AttributeField object>, 'passenger_flow': <movici_simulation_core.core.attribute.AttributeField object>}
cargo_flow
co2_emission
energy_consumption
length
nox_emission
passenger_flow

model

class Model(model_config: dict)

Bases: TrackedModel

Implementation of the traffic KPI model. Reads a csv with coefficients. Calculates segment CO2, NOx and energy consumption.

Asgarpour, S., Konstantinos, K., Hartmann, A., and Neef, R. (2021). Modeling interdependent infrastructures under future scenarios. Work in Progress.

add_road_coefficients()
add_tracks_coefficients()
add_waterway_coefficients()
build_state(state: TrackedState, dataset_name, schema: AttributeSchema)
co2_attr: UniformAttribute
ec_attr: UniformAttribute
initialize(state: TrackedState)

The initialize method is called when all of the state’s INIT attribute arrays are filled with data. This may be during the model engines initialization phase or during t=0. Data that is required for the model to initialize attribute may be published in another model’s t0-update, and the TrackedModelAdapter can wait for this to happen before calling initialize. When the simulation progresses to t>0 before the model’s INIT attributes have been filled, an Exception is raised, indicating that the model was not ready yet.

Model.initialize may raise NotReady to indicate that it does not have its required input data yet. This is for example useful if a model has a number OPT`ional required attributes of which at least one must be set. The model would check whether this is the case, and raise `NotReady if it is not. Once a model has succesfully run its initialize method, this method will not be called again for the duration of the simulation.

Parameters:

state – The model’s TrackedState object, managed by the TrackedModelAdapter

initialize_coefficients(data_handler: InitDataHandler, name: str)
modality: Literal['roads', 'tracks', 'waterways']
next_time() → Moment | None
nox_attr: UniformAttribute
reset_values()
scenario_parameters_tape: CsvTape | None = None
segments: TransportSegments | None
set_scenario_parameters(config, config_key: str)
setup(state: TrackedState, init_data_handler: InitDataHandler, schema: AttributeSchema, **_)

In setup, a model receives a state object, it’s config and other parameters. The goal of setup is to prepare the state by giving it information of the attributes it needs to track (by subscribing (INIT/SUB/OPT) or publishing (PUB) attributes) from which datasets. These attributes may be grouped together in EntityGroup classes or created directly. The main entry points for registering are:

  • state.add_dataset() for registering a bunch of EntityGroup classes for a certain dataset name at once

  • state.add_entity_group() for registering a single EntityGroup class (or instance) for a dataset name

  • state.register_attribute() for registering a single attribute in a dataset/entity_group combination

During setup there is no data available in the state. These will be downloaded automatically by the TrackedModelAdapter. However, additional datasets may be requested directly through the init_data_handler parameter.

Parameters:

  • state – The model’s TrackedState object, managed by the TrackedModelAdapter

  • settings – global settings

  • schema – The AttributeSchema with all registered attributes

  • init_data_handler – an InitDataHandler that may be used to retrieve additional datasets

  • logger – a logging.Logger instance

update(state: TrackedState, moment: Moment) → Moment | None

The update method is called for every update coming from the model engine. However it is only called the first time once all PUB attributes have their arrays filled with data. When the simulation progresses to t>0 before the model’s SUB attributes have been filled, an Exception is raised, indicating that the model was not ready yet.

Parameters:

  • state – The model’s TrackedState object, managed by the TrackedModelAdapter

  • moment – The current simulation Moment

Returns:

an optional Moment indicating the next time a model want to be woken up, as per the model engine’s protocol

convert_v1_v2(config)

Module contents