San Francisco recently completed the development of a citywide dynamic traffic assignment (DTA) model. The approach combines a mesoscopic DTA model with an explicit representation of every hill, street, traffic signal, stop sign, and transit route in the city. The model was found to be highly sensitive to the details of intersection coding and signal timing, thus a key challenge was ensuring the accuracy and reliability of all model inputs. This challenge was met by developing an open-source toolkit to automate the conversion of networks and demand from the static assignment model and other existing data sources, minimizing the potential for human error in the application process. A second advantage to this approach is that consistent scenarios can be run through the existing activity-based demand model, and the resulting change in demand can be readily fed into the DTA model. The model was applied to forecast alternative scenarios, including a bus-rapid transit scenario and a cordon pricing scenario, and shown to produce very different responses than the comparable static assignment model. This presentation and the accompanying paper will focus on San Francisco’s experience developing and applying the DTA model, on lessons learned from the process, and on recommendations for future improvements.