2. ANALYSIS METHODOLOGY The alternatives analysis process included several steps to compare the network performance of widening Lithia Pinecrest Road to other alternatives. The analysis used Aimsun simulation and forecasting software calibrated to represent existing traffic conditions using existing regional traffic model data, intersection traffic counts, and signal timings. Aimsun is a unique software because of its ability to analyze the performance of roadway networks under existing or potential improvements at both the large-scale regional scale or smaller intersection and corridor level. The Aimsun software allows multiple projects to be added or removed depending on the alternative to be tested. These tests can be performed and results processed and evaluated quickly. The benefit of Aimsun for this type of study is that it combines two scales of modeling, with regional model inputs (macroscopic) with intersection or corridor model inputs (microscopic), to create a hybrid model (mesoscopic) that offers a variety of analysis techniques. Aimsun macroscopic modeling operates similar to Cube modeling (Tampa Bay Regional Planning Model or TBRPM), which utilizes Origin-Destination (OD) matrices and link parameters in determining routing information. Aimsun microsimulation operates similar to Vissim microsimulation, utilizing traffic control features and car-following and lane changing models. These models allow for interaction between vehicles and can show model animation. Mesoscopic modeling, also called hybrid models, combines the individual vehicle modeling found in microsimulation with the higher level, regional modeling performed in macrosimulation. As described below and summarized in the Figure 2, the study team employed the following process to complete the modeling effort: 1. Use Cube software to run the Tampa Bay Regional Planning Model (TBRPM v8.2). Cube output includes link and node shapefiles and OD matrices. The link and node shapefiles are imported into Aimsun.
This allows the Aimsun model to use the same basic roadway network, roadway attributes, centroid data, and Origin-Destination (OD) data as the TBRPM. 2. While in Aimsun, run a macroscopic model and compare with results of the Cube output. If the results meet at least a 95% match, the subarea network was created. 3. Once the modeling limits were selected, Hillsborough County provided the traffic counts, signal timings, and funded or committed projects within the study area. Intersection and roadway geometries, along with intersection control types, were updated to create an Existing plus Committed (E+C) Model, which was used as the starting point for all the alternative sets that were analyzed. 4. Microscopic models were run for the E+C model to develop AM and PM volumes. The output from the E+C model was compared to the count data supplied by the County. The Aimsun model went through a calibration process of adjusting section and turn parameters to make the AM and PM Aimsun volumes match the count data. When the majority of turning movement volumes matched between these two sources, the volumes were finalized. These volumes became the basis for all alternative set models, so all alternatives were analyzed with a consistent set of volumes. 5. Mesoscopic modeling was performed on the E+C Model and results can be found in Section 3.3. Another feature of Aimsun mesoscopic modeling that was used in this analysis is dynamic assignment, which allows the traffic to reroute at specified intervals to better mimic how drivers act in the real world. 6. The E+C Model was used as the starting point for the Existing plus Committed plus Lithia Pinecrest Widening (E+C+LP) Alternative, as well as the alternative sets 1 through 7. The seven alternative sets were compared to the E+C+LP Model (see Section 4 for results).
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