Dependence-Preserving Approach to Synthesizing Household Characteristics

Abstract

One effective approach to study day-to-day traveler behavior is through the activity-based traffic demand model, in which all travelers are treated as individual agents and interact under a computation-intensive framework. Nevertheless, because of high survey costs, low response rate, and privacy concerns, detailed household and personal characteristics are usually unavailable. Various population synthesizers were therefore proposed to reconstruct a methodologically rigorous estimate of household characteristics from different surveys. For instance, the iterative proportional fitting (IPF) algorithm is used to synthesize the full population from the public use microdata sample (PUMS) and Census Summary File 3 (SF3) in the popular activity-based traffic demand model, TRANSIMS. However, some fundamental limitations of IPF (e.g., zero cells in the contingency table as a result of small sample size) have drawn sufficient attention and resulted in the development of enhanced IPF algorithms and other strategies. This paper proposes a copula-based method to synthesize household characteristics that preserves marginal distributions and dependence structure between variables. The proposed method is tested for the state of Iowa, and the results are compared with the IPF approach of TRANSIMS. The synthesized households resulted in the same local SF3 statistics at each block group. But having similar inter-variable correlations as described in the PUMS suggest the applicability of the copula-based approach. Because marginal distributions and dependence structure can be faithfully preserved, the proposed method could be a suitable alternative to synthesize realistic agent characteristics for further activity-based traffic demand modeling.