Synthetic Population Generation with Multilevel Controls: A Fitness‐Based Synthesis Approach and Validations. (15th May 2014)

• Record Type: Journal Article
• Authors: Ma, Lu; Srinivasan, Sivaramakrishnan
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 30:Number 2(2015:Feb.)
• Page Start: 135
• Page End: 150
• Publication Date: 2014-05-15
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12085
• Languages: English
• ISSNs: 1093-9687

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>The application of disaggregate models for predictions and policy evaluations requires as inputs detailed information on the socioeconomic characteristics of the population. The early procedure developed for population synthesis involved the generation of a joint multiway distribution of all attributes of interest using iterative proportional fitting (IPF). Recognizing its limitations, including the inability to deal with multilevel controls, several alternate methods have been proposed in the last few years. This article presents a methodology called the fitness‐based synthesis (FBS) that directly generates a list of households to match several multilevel controls without the need for determining a joint multiway distribution. The application and validation results demonstrate both the feasibility of the approach and its improved performance relative to the IPF and methods using fewer control tables. This article also presents a comprehensive validation of the synthetic populations against the true populations and thereby demonstrates the ability of the FBS method to generate the multidimensional correlations among the attributes. The number of iterations to terminations is found to be between one and three times the number of households to be synthesized. In sum, the FBS is an efficient and scalable methodology that is easy to implement and as such is a valuable tool for generating the detailed socioeconomic characteristics need for applying disaggregate travel‐demand forecasting models.</p> </abstract>