Model-Based Synthesis of Household Travel Survey Data in Small and Midsize Metropolitan Areas

Abstract

Household travel data synthesis–simulation has become a promising alternative or supplement to survey data from both small urban areas and large metropolitan regions in which data are expensive to collect or the data required to support the planning process have become outdated. This paper proposes and applies model-based approaches [i.e., small area estimation (SAE) methods] to synthesize household travel characteristics. The proposed methods address the sampling-bias concerns in the existing methods. Specifically, three SAE methods–-the generalized regression estimators method, the empirical best linear unbiased predictor (EBLUP) method, and the synthetic method (an EBLUP without random area effects)–-are applied to synthesize household travel characteristics at both census tract and individual levels. The SAE framework of synthesizing household travel characteristics is demonstrated with the National Household Travel Survey data and the Census Transportation Planning Package data in the Des Moines metropolitan area in central Iowa. Results indicate that SAE methods are promising approaches to synthesize unbiased aggregate and disaggregate household travel characteristics by incorporating population auxiliary information and local, small-household travel survey data. The proposed data synthesis methods and analysis findings will provide a useful tool for practitioners, planners, and policy makers in transportation analyses. The paper also points out that by linking population synthesis with the travel data simulation framework described here, this method could be of broad application in transportation planning.