A hierarchical mixture modeling framework for population synthesis
Authored by Lijun Sun, Alexander Erath, Ming Cai
Date Published: 2018
DOI: 10.1016/j.trb.2018.06.002
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Abstract
Synthetic population is a key input to agent-based urban/transportation
microsimulation models. The objective of population synthesis is to
reproduce the underlying statistical properties of real population based
on available microsamples and marginal distributions. However,
characterizing the joint associations among a large set of attributes is
challenging because of the curse of dimensionality, in particular when
attributes are organized in a hierarchical household-individual
structure. In this paper, we use a hierarchical mixture model to
characterize the joint distribution of both household and individual
attributes. Based on this model, we propose a framework of generating
representative household structures in population synthesis. The
framework integrates three models: (1) probabilistic tensor
factorization, (2) multilevel latent class model, and (3) rejection
sampling. With this framework, one can generalize not only the
associations of within- and cross-level attributes, but also reproduce
structural relationships among household members (e.g., husband-wife).
As a case study, we implement this framework based on the household
interview travel survey (HITS) data of Singapore, and then use the
inferred model to generate a synthetic population pool. This model
demonstrates great potential in reproducing the underlying statistical
distribution of real population. The generated synthetic population can
serve as a replacement for census in developing agent-based models, with
privacy and confidentiality being protected and preserved. (C) 2018
Elsevier Ltd. All rights reserved.
Tags
Population synthesis
Generation
Multilevel latent class
Mixture model
Probabilistic tensor factorization
Generalized raking
Latent class