**Author details**

*Transportation Systems Analysis and Assessment*

Iterative

Iterative proportional updating (IPU)

Combinatorial optimization

Markov processbased approach

Fitness-based synthesis (FBS)

Emerging approaches

**Table 1.**

(IPF)

proportional fitting

**Approach Advantage(s) Disadvantage(s)**

Synthesized estimates maintain the same odds ratios as those in the

Addresses the issue of control for individual-level attributes and joint distributions of personal

Generally simpler and more direct

Computationally efficient Described in 23 computational steps that can be easily coded in most programming languages

Fast and flexible with the possibility for application to both households and employment

Truly synthesizes populations instead of cloning them Meets the demand for large-scale microsimulation scenarios Can handle both continuous and

No need for determining a joint multiway distribution

Addresses the notorious IPF issues

Addresses all disadvantages of IPF

Does not provide an answer to the zero-cell problem in the public use microdata

Limited number of attributes that can be

Implementation is limited to a proprietary

Resource-demanding and needs multicore,

Requires extensive knowledge of computer

Requires extensive knowledge of the sparse matrix technique package in MATLAB that is based on high-level matrix programming

Requires advanced expertise in Python and makes heavy use of the Pandas and

Limited successful applications compared

for numerical computation

NumPy libraries

to IPF

Cannot overcome the zero-marginal problem that may result due to nonexistence of a certain attribute in the households of a certain geographic area

Unable to control for statistical distributions of both household- and

individual-level attributes

synthesized per agent

computer program

parallel computers

programming Difficult to trace errors Refinement for specific scenarios or locations requires substantial redevelopment of the computer algorithm

sample (PUMS)

Most studied and improved approach with more than 20 years of continuous refinements Widely available with ready-touse implementations in several computer programming languages

sample table

characteristics

than IPF

scenarios

discrete variables

of zero-cell problems

Scalable and adaptive

approach

*Key advantages and disadvantages of population synthesis approaches.*

Overall, this study provides a critical review and comprehensive synthesis of population synthesis approaches that can serve as a valuable reference to future efforts focusing on population synthesis for activity- and agent-based transportation models.

This study was performed in support of the project "Technology Influence on Travel Demand and Behaviors" that was sponsored by the US Department of Transportation Office of the Assistant Secretary for Research and Technology (OST-R) through the Southeastern Transportation Research, Innovation, Development, and Education (STRIDE) Center under Contract No. 69A3551747104 with matching

funds from the Alabama Department of Transportation (ALDOT).

**12**

**Acknowledgements**

Ossama E. Ramadan and Virginia P. Sisiopiku\* University of Alabama at Birmingham, Birmingham, AL, USA

\*Address all correspondence to: vsisiopi@uab.edu

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
