Abstract
Activity-based models (ABMs) that simulate travel are becoming commonplace because of their value in supporting analysis of policy-driven scenarios. Because of the complex nature of ABMs, only a limited number of data sources provide the detail necessary for the estimation and calibration of these models. Of those sources, household travel surveys are becoming increasingly central to the development and calibration of ABMs. These models mimic rational decision making and use a hierarchical decision-making framework that prioritizes mandatory travel and activities, resulting in constrained time for other, nonmandatory activities. Therefore, it is critical that expanded household surveys represent mandatory travel and activity characteristics accurately. Traditionally, household surveys have been expanded to match household-level demographics, such as household size and number of vehicles. More recent weighting frameworks have included person-level demographics, such as worker status and age. However, there is limited research, if any, looking into the role of employment-level attributes (e.g., journey-to-work flow data) within the weighting procedure. This research built on the body of work in the areas of survey expansion and synthetic population generation to incorporate two employment-level variables, industry-level employment totals and home-to-work flow patterns, in the expansion process. Further, the employment-level variables were matched at different spatial resolutions: (a) industry-level employment totals were matched at the regionwide level, and (b) home-to-work patterns were matched at the subregional level to improve travel duration distributions. The resulting weights were then contrasted with results from traditional expansion methods by summarizing a variety of variables that are suitable for model validation.
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