Comparing Microscopic Activity-Based and Traditional Models of Travel Demand: An Austin Area Case Study

Two competing approaches to travel demand modeling exist today. The more traditional “4-step” travel demand models rely on aggregate demographic data at a traffic analysis zone (TAZ) level. Activity-based microsimulation methods employ more robust behavioral theory while focusing on individuals and households. While currently not widely used in practice, many modelers believe that activity-based approaches promise greater predictive capability, more accurate forecasts, and more realistic sensitivity to policy changes. Little work has examined in detail the benefits of activity-based models, relative to more traditional approaches. In order to better understand the tradeoffs between these two methodologies, this paper examines model results produced by both, in an Austin, Texas application. Results of the analysis reveal several differences in model performance and accuracy. In general, activity-based models are more sensitive to changes in model inputs, supporting the notion that aggregate models ignore important behavioral distinctions across the population. However, they generally involve much more calibration and application effort, in order to ensure that synthetic populations match key criteria and that activity schedules match surveyed behaviors, while being realistic and consistent across household members.