Creating synthetic baseline populations

Beckman, Richard J.; Baggerly, Keith A.; McKay, Michael D.

doi:10.1016/0965-8564(96)00004-3

Cited by 326 publications

(342 citation statements)

References 8 publications

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“…This method is commonly known as Iterative Proportional Fitting (IPF) method and was introduced in transportation literature by Duguay et al (1976) to synthesize households survey data. It was later used by Beckman et al (1996) to create synthetic population for TRANSIMS (LaRon et al, 1996) using census cross tabulations and sample. Since then IPF has been the workhorse for synthesizing population for activity based travel demand and land use models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the second step, cloning of the sample using the tted CT was performed. Fractions in the CT table were dealt with running a Monte Carlo simulation as suggested by Beckman et al (1996). Note that, for IPF, the completion of conditionals does not matter, as they have to be converted to marginals anyway.…”

Section: Ipf Based Synthesismentioning

confidence: 99%

“…We used the standard two step process outlined in Beckman et al (1996) to generate synthetic population using IPF procedure (i.e. tting, and cloning process).…”

Section: Ipf Based Synthesismentioning

confidence: 99%

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Simulation based population synthesis

Farooq

Bierlaire

Hurtubia

et al. 2013

Transportation Research Part B: Methodological

150

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1Microsimulation of urban systems evolution requires synthetic population as a key input. Currently, the focus is on treating synthesis as a tting problem and thus various techniques have been developed, including Iterative Proportional Fitting (IPF) and Combinatorial Optimization based techniques. The key shortcomings of these procedures include: a) tting of one contingency table, while there may be other solutions matching the available data b) due to cloning rather than true synthesis of the population, losing the heterogeneity that may not have been captured in the microdata c) over reliance on the accuracy of the data to determine the cloning weights d) poor scalability with respect to the increase in number of attributes of the synthesized agents. In order to overcome these shortcomings, we propose a Markov Chain Monte Carlo (MCMC) simulation based approach. Partial views of the joint distribution of agent's attributes that are available from various data sources can be used to simulate draws from the original distribution. The real population from Swiss census is used to compare the performance of simulation based synthesis with the standard IPF. The standard root mean square error statistics indicated that even the worst case simulation based synthesis (SRMSE = 0.35) outperformed the best case IPF synthesis (SRMSE=0.64). We also used this methodology to generate the synthetic population for Brussels, Belgium where the data

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Section: Literature Reviewmentioning

confidence: 99%

Section: Ipf Based Synthesismentioning

confidence: 99%

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Simulation based population synthesis

Farooq

Bierlaire

Hurtubia

et al. 2013

Transportation Research Part B: Methodological

150

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“…Figure 1 illustrates the NYC five-borough region in our simulation. We used a method developed by Beckman et al 1 to help extract the agent population from the US Census Bureau's Public Use Microdata files and Census aggregated data. 2 The model contains a total of 7,847,465 computer agents, or virtual people.…”

Section: Introductionmentioning

confidence: 99%

The Role of Subway Travel in an Influenza Epidemic: A New York City Simulation

et al. 2011

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The interactions of people using public transportation in large metropolitan areas may help spread an influenza epidemic. An agent-based model computer simulation of New York City's (NYC's) five boroughs was developed that incorporated subway ridership into a Susceptible-Exposed-Infected-Recovered disease model framework. The model contains a total of 7,847,465 virtual people. Each person resides in one of the five boroughs of NYC and has a set of socio-demographic characteristics and daily behaviors that include age, sex, employment status, income, occupation, and household location and membership. The model simulates the interactions of subway riders with their workplaces, schools, households, and community activities. It was calibrated using historical data from the 1957-1958 influenza pandemics and from NYC travel surveys. The surveys were necessary to enable inclusion of subway riders into the model. The model results estimate that if influenza did occur in NYC with the characteristics of the 1957-1958 pandemic, 4% of transmissions would occur on the subway. This suggests that interventions targeted at subway riders would be relatively ineffective in containing the epidemic. A number of hypothetical examples demonstrate this feature. This information could prove useful to public health officials planning responses to epidemics.

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“…Beckman et al described its use for generating a synthetic population, a data set that is statistically consistent with microdata and aggregate controls (22). For Beckman et al, the rationale for using IPF was to maintain the odds ratios in the contingency table, as these define the correlation structure (22). From the perspective of population modeling, using IPF is equivalent to estimating a log-linear model in which the aggregate controls define the model structure and the survey data define the previous distribution (23).…”

Section: Survey Calibrationmentioning

confidence: 99%

Using Survey Calibration and Statistical Matching to Reweight and Distribute Activity Schedules

Müller

Axhausen

2014

Transportation Research Record: Journal of the Transportation R

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The processes used to generate a 106-member agent population for a longrange study of 2030 travel in Switzerland are presented in this paper. This study was part of an effort to assess the effects of electric vehicles on the energy production and stability of the electric supply network. The process used well-established statistical methods—survey calibration and statistical matching. Both methods are described, and consistency with known approaches in transportation planning is shown. The paper introduces a new approach that allows exogenous specification of shares of activity types while maintaining the representativeness of the population: survey calibration is applied to satisfy these constraints, and statistical matching allows the joining of data sets with common variables. The discussion of the results for Switzerland focuses on the quality metrics available and highlights the links between the activity schedules and total shares of the activity types. Furthermore, the error introduced by the calibration and matching stages was analyzed and quantified, with special emphasis placed on uncontrolled sociodemographic and travel variables. The specified activity shares could be replicated almost perfectly; the resultant mean error in the uncontrolled variables was within the range of a few percentage points. Therefore, this approach is a viable alternative to a complicated estimation of an activity schedule model that would be necessary otherwise. Finally, two practical issues—data sets describing different populations and biased surveys—and their effect on the outcome were tested by suitably adjusting the input data.

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Creating synthetic baseline populations

Cited by 326 publications

References 8 publications

Simulation based population synthesis

Simulation based population synthesis

The Role of Subway Travel in an Influenza Epidemic: A New York City Simulation

Using Survey Calibration and Statistical Matching to Reweight and Distribute Activity Schedules

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