The San Francisco County Transportation Authority (the Authority) has had success with medium-scale Dynamic Traffic Assignment (DTA) in several real-life planning applications using its Northwest San Francisco Subarea DTA model . Soon after its development, the Authority began making plans to expand the subarea to accommodate the growing number of projects that wanted to use a DTA model in their analysis process and explore the possibility of eventually using DTA to replace the static network model in SF-CHAMP.

At a project-level, planners have found DTA to be a useful post-process to static traffic assignment to (1) understand the effect of projects that can be measured at the mesoscopic level, and (2) serve as a tidier transition from static traffic assignment into a traffic microsimulation model. At a higher level, planners are interested in incorporating DTA’s ability to more accurately evaluate reliability, transit/auto interactions, and operational treatments into an integrated modeling framework that is capable of evaluating changes in travel behavior (an integrated Activity-Based demand model and dynamic network model).

This presentation details the development process and results from the Authority’s experience building and maintaining a citywide DTA model. A mesoscopic simulation of a grid network is highly sensitive to network coding and decisions as small as how centroid connectors are placed. As such, the team collaboratively developed an open source toolset in order to accurately capture, manage, and translate information about every traffic signal, stop sign, roadway, and transit vehicle operating in the City of San Francisco. As much as possible, real data (as opposed to imputed data) was used to estimate traffic flow parameters. San Francisco’s hilly topography necessitated extensive field work in order to achieve this data-driven traffic flow parameter estimation. A series of scripts a create each DTA scenario from the SF-CHAMP demand model inputs and outputs. Network geometry and demand issues found during calibration were addressed “at their source” either at the top of the model chain (i.e. road geometry) or where they appeared (i.e. destination choice models). This process made SF-CHAMP more accurate and ensures behavioral consistency between the demand and network models.