Population Synthesis
The advent of activity-based microsimulation models of travel demand has provided the ability to simulate activity-travel patterns of individual travelers in time and space. The application of activity-based travel demand model systems requires the generation of synthetic populations whose attribute distributions match those of the general population within small geographies (say, a traffic analysis zone, census tract, block group, or block). The generation of synthetic populations has therefore received considerable attention in the profession and several synthetic population generators or population synthesizers have been developed and applied in various urban areas that are transitioning to or applying activity-based model systems. The Mobility Analytics Research Group developed a new heuristic algorithm for iteratively generating a representative synthetic population. In some older synthetic population generators, the distributions of household-level attributes match well with census distributions, but the distributions of person-level attributes are not adequately controlled to ensure the best possible match with census distributions. The new algorithm developed and incorporated in PopGen is capable of producing synthetic populations while controlling and matching both household-level and person-level attribute distributions. The PopGen software can be implemented easily and effectively for synthesizing populations while matching population controls in small geographies. PopGen has been applied and tested in a number of states, metropolitan planning organizations (MPOs), and research studies. PopGen is capable of synthesizing populations whose attribute distributions closely match those of the general population as documented in the census. The Mobility Analytics Research Group is working on the next generation of PopGen, which will incorporate full population evolution and socio-economic dynamics to evolve the population over time.
The development of PopGen was made possible through generous support provided by the Federal Highway Administration under its Exploratory Advanced Research Program (EARP). Additional support was provided by Arizona State University, Georgia Institute of Technology, and University of Connecticut through faculty start-up resources. In addition, a number of agencies have supported and sponsored the development, refinement, and enhancement of PopGen. In particular, Maricopa Association of Governments (MAG), Baltimore Metropolitan Council (BMC), Southern California Association of Governments (SCAG), and Colorado Department of Transportation (CDOT) have provided support for and implemented/tested PopGen.
The development of PopGen was made possible through generous support provided by the Federal Highway Administration under its Exploratory Advanced Research Program (EARP). Additional support was provided by Arizona State University, Georgia Institute of Technology, and University of Connecticut through faculty start-up resources. In addition, a number of agencies have supported and sponsored the development, refinement, and enhancement of PopGen. In particular, Maricopa Association of Governments (MAG), Baltimore Metropolitan Council (BMC), Southern California Association of Governments (SCAG), and Colorado Department of Transportation (CDOT) have provided support for and implemented/tested PopGen.
References |
The following publications offer details about the methods embedded in the PopGen software. Please cite these articles in any work that utilizes the PopGen synthetic population generator software system.
- Konduri, K.C., D. You, V.M. Garikapati, and R.M. Pendyala (2016) Application of an Enhanced Population Synthesis Model that Accommodates Controls at Multiple Geographic Resolutions. Transportation Research Record, Journal of the Transportation Research Board (forthcoming).
- Ye, X., K. Konduri, R.M. Pendyala, B. Sana, and P. Waddell (2009) A Methodology to Match Distributions of Both Household and Person Attributes in the Generation of Synthetic Populations. Proceedings of 88th Annual Meeting of the Transportation Research Board, National Research Council, Washington, D.C.
- Bar-Gera, H., K. Konduri, B. Sana, X. Ye, and R.M. Pendyala (2009) Estimating Survey Weights with Multiple Constraints Using Entropy Optimization Methods. Proceedings of 88th Annual Meeting of the Transportation Research Board, National Research Council, Washington, D.C.
- MARG (2016) PopGen: Synthetic Population Generator [online]. Mobility Analytics Research Group. Available at: http://www.mobilityanalytics.org/popgen.html, Accessed [Date of Access].
- Pendyala, R.M., K.C. Konduri, and K.P. Christian (2011) PopGen 1.1 User’s Manual. Lulu.com Publishers, USA.
- http://urbanmodel.asu.edu/popgen.html
- http://simtravel.wikispaces.asu.edu/Population+Synthesis
PopGen 1.1 |
Download and unzip (if applicable) all of the files below. Then, follow the instructions for installation exactly as specified. All files below may be downloaded to a single directory. PopGen 1.1 works with all recent versions of the Windows operating system.
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Software
Release: Apr 20, 2016 |
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PopGen 2.0Software
Release: April 20, 2016 Distributed Under: License Agreement Check Out: PopGen 2.0 Github PopGen 2.0 Presentation |
Download and unzip (if applicable) all of the files below. Then, follow the instructions for installation exactly as specified. All files below may be downloaded to a single directory. PopGen 2.0 works with all recent versions of the Windows operating system.
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PopGen 1.1 vs PopGen 2.0 |
Both PopGen 1.1 and PopGen 2.0 are fully functional software systems. They are some key differences between the two versions of the software that users may consider before selecting the specific version to use for an application.
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PopGen 1.1
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PopGen 2.0
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