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Sub-problem 3b - Page 1 of 2

ID# C103B01

Sub-Problem 3b: Using a Microscopic Simulation Model

While the HCM model for signalized intersections can be used to evaluate the performance of the U.S. 95/Styner Avenue-Lauder Avenue as we showed in sub-problem 3a, we should also consider the use of a microscopic simulation model. Why? The HCM model is only an approximate solution for conditions when demand exceeds capacity. Since it considers only macroscopic conditions, the HCM cannot provide the same level of accuracy as can a microscopic simulation model for high demand periods or when the flows from one intersection interact with the flows from an adjacent intersection.

There are several microscopic simulation models that can be used for this problem.  The application of microscopic simulation is covered in Chapter 34 of the HCM 2000.  You are encouraged to review this chapter to learn more about some of the advantages and disadvantages of using microscopic simulation models.  Here, we will use one such model, the CORSIM model developed by the Federal Highway Administration. It should be emphasized that the CORSIM model is being used here for illustrative purposes, and other microscopic simulation models are available that would work equally well.

Microscopic simulation models consider much more detail than macroscopic models such as the HCM. Individual vehicle interactions and the detailed operation of traffic controllers are two of the more important features of these microscopic models. This more detailed treatment of traffic flow and controller operations allows the models to consider more directly the oversaturated conditions that we found in sub-problem 3a. In addition, we can input the traffic flow data for all four time periods for this problem at once and not have to run several analyses as we did in sub-problem 3a. The models are also stochastic in nature, which means they explicitly account for the probabilistic nature of traffic flow and driver behavior. But there is a cost for this additional detail: more input data, more time required to ensure that the model is calibrated for local conditions, and multiple simulations in order to assure that statistically valid results are obtained.

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