Traffic Congestion and Level of Service

In support of the Temple Master Transportation Plan, mesoscopic models were developed to analyze operational performance of the existing roadway network using Transmodeler (version 6) software. TransModeler is a mesoscopic tool for conducting large-scale, detailed traffic simulations using origindestination data. Measures of effectiveness from the TransModeler simulations were used to establish baseline operations for future comparison.

The following information provides a summary of the operational analysis used for establishing baseline conditions using TransModeler. The study methodology is as follows:

1. Acquired data from the KTMPO travel demand model performed using the 2015 base year including: a. Existing roadway geometry b. Roadway functional class information c. Speed limits d. Daily origin-destination (O-D) metrics reflecting 2015 weekday trip patterns, beginning, ending, or passing through Temple 2. Acquired signal timing data from the City of Temple for all signalized intersection within the City limits. 3. Used historical ADT from the TxDOT Statewide Traffic Analysis and Reporting System (STARS) to develop AM and PM peak hour factors that were then applied to the daily O-D data to provide 2015

AM peak hour and PM peak hour O-D matrices. 4. Determined 2021 baseline AM peak hour and PM peak hour O-D matrices using 2021 existing volume counts and traffic growth rates determined from historical traffic counts obtained from

STARS. A growth rate of 2.0% was applied over a six-year period. 5. Developed a simulation network of Temple’s transportation system using Caliper TransModeler

Version 6.0™ to model existing traffic conditions of the roadway network. 6. Analyzed existing conditions using TransModeler to compile intersection turning movement counts, intersection level of service, and delay. 7. Compared output produced in step 6 with existing peak hour counts taken in 2019 pre-pandemic conditions to evaluate the validity of the traffic demand model data using a square error (% RSME) test. 8. Calibrated the baseline model using the results of Step 7 and to reflect more realistic field conditions. 9. In accordance with the 2010 Highway Capacity Manual (HCM), reviewed the results of the baseline simulation model runs to evaluate the quality of traffic flow.

By developing a baseline condition, the resulting model will allow operational and capacity issues to be identified and support review and comparative analysis of the effects of modified lane configurations, traffic control, and any additional mitigations made to the roadway network on the systems operational performance for the upcoming future conditions.

Measures of effectiveness (MOEs) were output from the TransModeler simulation runs to evaluate operational performance of the AM and PM peak hours in the baseline conditions. These MOEs include intersection level of service (LOS), total network delay, total vehicle-miles traveled (VMT), segment delay, and segment volume. Figure 52 provides an example of a simulation run displaying traffic backup along 31st Street at the Loop 363 interchange.