Travel forecasting affects transportation professionals on a number of levels, from planners and stakeholders to decision-makers and members of the general public. Currently, forecasting is assessed with trip-based travel demand models. However, these models are considered static, since the process of collecting the information doesn’t take into account actual travel demand in a system around the clock.
Another way to measure trip-based travel demand is through a more dynamic data-analysis process, a procedure that researchers at TTI were asked to test and compare with the current static model. Like static measurements, dynamic traffic assignments (DTAs) are applied to determine traffic volumes resulting from traffic demand, but are better suited to assessing variations in traffic patterns involving turn and managed lanes. By looking at dynamic, simulation-based models, researchers can determine if they could replace the less reliable static models for future traffic forecasting.
For additional information about this project and the results, please visit the Integrating Traditional Trip-based Models with Dynamic Traffic Assignment Project Page.