Abstract
Generating synthetic baseline populations is a fundamental step of agent-based modeling and simulation, which is growing fast in a wide range of socio-economic areas including transportation planning research. Traditionally, in many commercial and non-commercial microsimulation systems, the iterative proportional fitting (IPF) procedure has been used for creating the joint distribution of individuals when combining a reference joint distribution with target marginal distributions. Although IPF is simple, computationally efficient, and rigorously founded, it is unclear whether IPF well preserves the dependence structure of the reference joint table sufficiently when fitting it to target margins. In this paper, a novel method is proposed based on the copula concept in order to provide an alternative approach to the problem that IPF resolves. The dependency characteristic measures were computed and the results from the proposed method and IPF were compared. In most test cases, the proposed method outperformed IPF in preserving the dependence structure of the reference joint distribution.
Highlights
Large-scale micro-simulations using agent-based models have gained wide popularity in recent years in various fields of socio-economic studies [1] including transportation planning [2] and land use [3]
The primary concept of iterative proportional fitting (IPF) is to maintain the dependence structure from the disaggregated data and alter the joint distribution to fit the marginal distribution of the attributes from the aggregated data. (We will use the term ‘marginal distribution’ and ‘margin’ interchangeably.) Since the inception by Beckman et al, there has been much research following this path: see [4,6]
This indicates that the proposed method preserves the dependence structure of the reference joint distribution, while the other methods (IPF and QP) often fail to maintain the dependence structure when the target margins are significantly different from those of the reference
Summary
Large-scale micro-simulations using agent-based models have gained wide popularity in recent years in various fields of socio-economic studies [1] including transportation planning [2] and land use [3]. The primary concept of IPF is to maintain the dependence structure from the disaggregated data and alter the joint distribution to fit the marginal distribution of the attributes from the aggregated data. Kao et al [9] proposed a copula based approach to synthesizing households in order to preserve the dependence structure They combine target margins using Gaussian copula, whose covariance matrix is determined from the reference joint distribution (possibly represented by samples). In synthetic population generation, there are many research issues such as household–individual hierarchy and aggregation data inconsistency, the problem focused in this paper is the construction of a joint distribution from a given reference joint distribution and target margins.
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