PyANGDTA.DTASimulator

class DTASimulator

Aimsun Next Dynamic Traffic Assignment simulator front-end.

Details

This is the base class for all dynamic traffic assignment simulators. For example: - GAimsunSimulator - AMesoDTASimulator The simulator can hold either a Stochastic Route Choice simulation or an Dynamic User Equilibrium simulation.

Synopsis

Methods

Virtual methods

Note

This documentation may contain snippets that were automatically translated from C++ to Python. We always welcome contributions to the snippet translation. If you see an issue with the translation, you can also let us know by creating a ticket on https:/bugreports.qt.io/projects/PYSIDE

__init__([manager=None])
Parameters:

manager – DTAManager

abstractmethod activateOutputs()

Active statistics.

abstractmethod canFinishNetworkLoadingDTA()
Return type:

bool

Returns true if simulation can finish the DNL simulation. This is when the simulation time is greater than the total simulation duration.

abstractmethod configureDTAObjects()

It binds objects from the dynamic network loading model(micro or meso) to DTA objects.

abstractmethod configureIteration(iterationNumber)
Parameters:

iterationNumber – int

This function is provided for convenience. It has to be reimplemented by the micro or meso simulators in order to initialize its respective internal classes. It’s called every time before starting the iteration simulation.

deactivateOutputs()

Deactive statistics.

abstractmethod dynamicalRouteChange()

This function is provided for convenience. It has to be reimplemented by the micro or meso simulators in order to run the dynamic route change. See Aimsun Next Hybrid Meso-Micro model explanation in the manual.

abstractmethod getTime()
Return type:

float

Returns the current time.

abstractmethod getTimeSta()
Return type:

float

Returns the current time using the initial time as the starting time. During the warm-up the return value is the initial time less the corresponding warm-up simulation time.

abstractmethod isStationary()
Return type:

bool

Returns true if current time is greater than the warm-up time.

abstractmethod postIter(iscancel)
Parameters:

iscancel – bool

This function is provided for convenience. It has to be reimplemented by the micro or meso simulators in order to initialize its respective internal classes. It’s called every time a iteration simulation is finished.

abstractmethod simulateStepOneShot()

Simulated a one shot simulation step.

slowOutputsActive()
Return type:

bool

Returns the current status of statistics. True means that is not gathering statistics false is gathering.

class DTAIterationTask

Synopsis

Methods

Note

This documentation may contain snippets that were automatically translated from C++ to Python. We always welcome contributions to the snippet translation. If you see an issue with the translation, you can also let us know by creating a ticket on https:/bugreports.qt.io/projects/PYSIDE

Detailed Description

Dynamic traffic assignment based on equilibrium information for each iteration. It has information about simulation times: - DNL: Dynamic Network Loading - SP: Shortest Path calculation - MSA: Moving Successive Averages calculation and vehicles: - Total number of vehicles waiting to enter. This is the total number of vehicles in virtual queues. - Total number of vehicles inside. This is the total number of vehicles that are inside the network being moved. - Total number of vehicles outside. This is the total number of vehicles that left the network.

PyANGDTA.DTASimulator.DTAIterationTask.dnl_time
PyANGDTA.DTASimulator.DTAIterationTask.sp_time
PyANGDTA.DTASimulator.DTAIterationTask.msa_time
PyANGDTA.DTASimulator.DTAIterationTask.rgap_time
PyANGDTA.DTASimulator.DTAIterationTask.waitout
PyANGDTA.DTASimulator.DTAIterationTask.inside
PyANGDTA.DTASimulator.DTAIterationTask.goneout
__init__()
totalTime()
Return type:

float

Returns the total simulation time of this task/iteration.