A Bayesian network approach for population synthesis
Authored by Lijun Sun, Alexander Erath
Date Published: 2015
DOI: 10.1016/j.trc.2015.10.010
Sponsors:
Singapore National Research Foundation
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Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
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Abstract
Agent-based micro-simulation models require a complete list of agents
with detailed demographic/socioeconomic information for the purpose of
behavior modeling and simulation. This paper introduces a new
alternative for population synthesis based on Bayesian networks. A
Bayesian network is a graphical representation of a joint probability
distribution, encoding probabilistic relationships among a set of
variables in an efficient way. Similar to the previously developed
probabilistic approach, in this paper, we consider the population
synthesis problem to be the inference of a joint probability
distribution. In this sense, the Bayesian network model becomes an
efficient tool that allows us to compactly represent/reproduce the
structure of the population system and preserve privacy and
confidentiality in the meanwhile. We demonstrate and assess the
performance of this approach in generating synthetic population for
Singapore, by using the Household Interview Travel Survey (HITS) data as
the known test population. Our results show that the introduced Bayesian
network approach is powerful in characterizing the underlying joint
distribution, and meanwhile the overfitting of data can be avoided as
much as possible. (C) 2015 Elsevier Ltd. All rights reserved.
Tags
Agent
Model
transportation