Master Transportation Plan Medium-Term Modeling Analysis
still showed traffic levels significantly different than the count data. For these, specific queries were made to do individual adjustments.
Figure 4: Calibration Process
A detailed description of the results of these steps is presented in the following sections.
3.2.1 Count Data Preparation TCDS data was obtained for 144 segments throughout the study area. Out of these locations, 51 were selected in key locations close to the primary and secondary priority bond projects, to guarantee a close representation of traffic; and 15 additional locations were selected in the Core area, in the interest of Connecting the Core study. All the selected locations are shown in Figure 1. The TCDS data provides only Average Annual Daily Traffic (AADT) for all the locations, as the TDM is meant to represent a typical weekday, a seasonality factor was needed to have comparable numbers. We selected the five permanent count locations in the study area, these locations have traffic counts for every day of the year, allowing to do seasonality analysis. For these locations, we extracted the count data for all the typical weekdays (Tuesday-Thursday) in April 2023, representing a typical school-month and estimated the factor between AADT and typical day, obtaining an average value of 1.09 (see Table 3). This seasonal adjustment factor is applied to all count locations, providing targets for an average-week day to compare with the modelled daily results.
Table 3: Seasonality Calculation TCDS Count ID AADT
Traffic Counts Apr 2023
Seasonality Factor
A109 A130 A193 A301 S524
155,190 144,707 101,772 20,184 128,273
158,457 159,950 110,844 23,777 134,586
1.02 1.11 1.09 1.18 1.05
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