The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.

The underlying structure of road networks (e.g., circuity, relative discontinuity) contributes to the travel time perception of travelers. This study considers additional factors (e.g., arrival flexibility, access to traffic information) and tests nonlinearities linking perception of travel time. These factors are linked to four categories according to time perception research in psychology: temporal relevance, temporal uncertainty, and temporal expectancies; task complexity, absorption, and attentional deployment; and affective elements. This study estimates the relationship on data collected from commuters recruited from a previous GPS-based study in the Minneapolis-St. Paul region consisting of trips from home to work and back. For these work trips, the subjects’ self-reported travel times and the subjects’ travel times measured by GPS devices were collected. The results indicate that nonlinearities are present for road network attributes. Furthermore, the additional factors (e.g., arrival flexibility, access to traffic information) influence the travel time perception of travelers.