Simulation modelling is an increasingly popular and effective tool for
analyzing a wide variety of dynamical problems those associated with complex
processes which cannot readily be described in analytical terms.
Usually, these processes are characterized by the interaction of many system
components or entities whose interactions are complex in nature.
Specifically, simulation models are mathematical/logical representations of
real-world systems, which take the form of software executed on a digital
computer in an experimental fashion.
The most important advantage is that these models are by no means exhaustive.
Traffic simulation models have a large variety of applications in the required
fields.
Now-a-days they become inevitable tools of analysis and interpretation of real
world situations especially in Traffic Engineering.
The following are some situations where these models can find their scope.
- When mathematical or analytical treatment of a problem is found
infeasible or inadequate due to its complex nature.
- When there is some doubt in the mathematical formulation or results.
- When there is a need of an animated view of flow of vehicles to study
their behaviour.
It is important to note that simulation can only be used as an auxiliary tool
for evaluation and extension of results provided by other conceptual or
mathematical formulations or models.
Traffic simulations models can meet a wide range of requirements:
- Evaluation of alternative treatments
- Testing new designs
- As an element of the design process
- Embed in other tools
- Training personnel
- Safety Analysis
Traffic simulation models can be classified based on different criteria.
Figure 1 shows various types of classification.
In a broader sense, they can be categorized into continuous and discrete ones
according to how the elements describing a system change their states.
The latter is again classified into two.
- Discrete time based models
- Discrete event based models
Figure 1:
Classification of Traffic simulation models
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The first, divides time into fixed small intervals and within each interval the
simulation model computes the activities which change the states of selected
system elements.
For some specific applications, considerable savings in computational time can
be achieved by the use of event based models where scanning is performed based
on some abrupt changes in the state of the system (events).
However the discrete time models could be a better choice where the model
objectives require more realistic and detailed descriptions.
According to the level of detailing, simulation models can be classified into
macroscopic, mesoscopic and microscopic models.
A macroscopic model describes entities and their activities and interactions at
a low level of detail.
Traffic stream is represented in an aggregate measure in terms of
characteristics like speed, flow and density.
A mesoscopic model generally represents most entities at a high level of detail
but describes their activities and interactions at a much lower level of
detail.
A microscopic model describes both the system entities and their interactions
at a high level of detail.
Car following models and lane changing models are some significant examples.
The choice of a particular type of model depends on the nature of the problem
of interest.
Depending on the type of processes represented by the model, there are
deterministic and stochastic models.
Models without the use of any random variables or in other words, all entity
interactions are defined by exact mathematical/logical relationships are called
deterministic models.
Stochastic models have processes which include probability functions.
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![\includegraphics[width = 14 cm]{qftrafficsimulation}](img1.png)