From Aggregate Methods to Microsimulation: Assessing Benefits of Microscopic Activity-Based Models of Travel Demand

Document Type

Journal Article

Publication Date


Subject Area

operations - capacity, operations - traffic, planning - methods, land use - planning, ridership - demand, economics - benefits, mode - mass transit, mode - subway/metro


Travel models (Travel demand), Travel demand, Travel behavior, Transit, Traffic capacity, Traffic analysis zones, Public transit, Microsimulation, Metropolitan planning organizations, Mass transit, Local transit, Highway capacity, Austin (Texas), Activity-based models, Activity choices


Two competing approaches to travel demand modeling exist today. The more traditional four-step travel demand models rely on aggregate demographic data at a traffic analysis zone (TAZ) level. Activity-based microsimulation methods use more robust behavioral theory while focusing on individuals and households. Although the vast majority of U.S. metropolitan planning organizations continue to rely on traditional models, 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. To understand better the trade-offs between these two methodologies, results produced by both were modeled in an Austin, Texas, application. Three scenarios are examined: a base scenario, a scenario with expanded capacity along two key freeways, and a centralized-employment scenario. Results of the analysis revealed several differences in model performance and accuracy in terms of replicating travel survey and traffic count data. Such distinctions largely emerged through differing model assumptions. In general, activity-based models were more sensitive to changes in model inputs, supporting the notion that aggregate models ignore important behavioral distinctions across the population. However, they involved more effort and care in data manipulation, model calibration, and application to mimic behavioral processes better at a finer resolution. Such efforts help ensure that synthetic populations match key criteria and that activity schedules match surveyed behaviors, while being realistic and consistent across household members.