Page 186 - DCAP601_SIMULATION_AND_MODELING

Simulation and Modelling

Notes time. These parameters influence driver behaviors such as when and how long it takes a driver
to change lanes, how much distance a driver leaves between itself and the car in front of it, and
how quickly it starts to accelerate through an intersection. Adjusting these parameters has a
direct effect on the amount of traffic volume that can traverse through the modeled roadway
network by making the drivers more or less aggressive. These are examples of calibration
parameters that can be fine-tuned to match up with characteristics observed in the field at the
study location. Most traffic models will have typical default values but they may need to be
adjusted to better match the driver behavior at the location being studied.
Model verification is achieved by obtaining output data from the model and comparing it to
what is expected from the input data. For example in traffic simulation, traffic volume can be
verified to ensure that actual volume throughput in the model is reasonably close to traffic
volumes input into the model. Ten percent is a typical threshold used in traffic simulation to
determine if output volumes are reasonably close to input volumes. Simulation models handle
model inputs in different ways so traffic that enters the network, for example, may or may not
reach its desired destination. Additionally, traffic that wants to enter the network may not be
able to if any congestion exists. This is why model verification is a very important part of the
modeling process.

The final step is to validate the model by comparing the results with what’s expected based on
historical data from the study area. Ideally, the model should produce similar results to what
has happened historically. This is typically verified by nothing more than quoting the R2 statistic
from the fit. This statistic measures the fraction of variability that is accounted for by the model.
A high R2 value does not necessarily mean the model fits the data well. Another tool used to
validate models is graphical residual analysis. If model output values are drastically different
than historical values, it probably means there’s an error in the model. This is an important step
to verify before using the model as a base to produce additional models for different scenarios
to ensure each one is accurate. If the outputs do not reasonably match historic values during the
validation process, the model should be reviewed and updated to produce results more in line
with expectations. It is an iterative process that helps to produce more realistic models.

Validating traffic simulation models requires comparing traffic estimated by the model to
observed traffic on the roadway and transit systems. Initial comparisons are for trip interchanges
between quadrants, sectors, or other large areas of interest. The next step is to compare traffic
estimated by the models to traffic counts, including transit ridership, crossing contrived barriers
in the study area. These are typically called screenlines, cutlines, and cordon lines and may be
imaginary or actual physical barriers. Cordon lines surround particular areas such as the central
business district or other major activity centers. Transit ridership estimates are commonly
validated by comparing them to actual patronage crossing cordon lines around the central
business district.
Three sources of error can cause weak correlation during calibration: input error, model error,
and parameter error. In general, input error and parameter error can be adjusted easily by the
user. Model error however is caused by the methodology used in the model and may not be as
easy to fix. Simulation models are typically built using several different modeling theories that
can produce conflicting results. Some models are more generalized while others are more
detailed. If model error occurs as a result of this, in may be necessary to adjust the model
methodology to make results more consistent.
In order to produce good models that can be used to produce realistic results, these are the
necessary steps that need to be taken in order to ensure that simulation models are functioning
properly. Simulation models can be used as a tool to verify engineering theories but are only
valid if calibrated properly. Once satisfactory estimates of the parameters for all models have
been obtained, the models must be checked to assure that they adequately perform the functions
for which they are intended. The validation process establishes the credibility of the model by

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