Assessing traffic diversion due to tolling is an important activity in toll modeling. While traditional macro models do a reasonable job in forecasting regional traffic flow, its ability to forecast traffic diversion due to tolling is questionable because it lacks network details. This paper, using SR-167 extension comprehensive tolling study as an example, signifies the value of including intersection geometry and traffic control in toll scenario modeling. SR 167 is a proposed new limited access facility connecting a dead-ended freeway with a major seaport with several local arterials as its potential alternate routes in its close proximity. These potential alternate routes have many signalized intersections with large delays. Due to resources and time constraints, a macro model (four-step model) was first used to analyze traffic usage and diversions associated with various tolled scenarios. The initial macro model results showed nearly two-thirds traffic would be diverted to the local arterials if the new roadway is tolled. Upon close examination of the initial results, staff made a strong case and convinced the executives and stakeholders to extend the project time to allow for more detailed analysis by taking intersection delay on local arterials into consideration. A meso model was built for a sub-area to analyze toll segment volumes with 50 plus intersections coded with geometry and signal times where applicable. The meso model, used trip tables generated by the macro model as inputs and reassigned the trips using dynamic traffic assignment algorithm. Indeed, the results of meso-scopic model with (DTA) are different than the macro model. PM peak period and AM peak period toll segment volumes were significantly higher than macro model results. This paper discusses the approach taken to build meso model and compares the results of toll segment volumes with macro model. It also compares truck vs. auto usage on the toll segments and gross toll revenues. This study is currently on-going and is scheduled for completion by the end of this year. Authors will present traffic model and revenue forecasting results along with how the executives and stakeholders use the information in making informed decisions.