3.5 Synthetic Population

Overview

In trip-based models, trip rates are applied to aggregate households grouped in Traffic Analysis Zones (TAZs) to generate trips. On the other hand, in an activity-based model (ABM), choices involving activities and trips are simulated for each of the individual persons in households. Hence, it is necessary to first develop a “synthetic population” of the regions’ residents. Synthetic population is a list of households and persons that is based on observed or forecasted distributions of socioeconomic attributes and created by sampling detailed Census microdata. This produces individual household agents and individual person agents that are subjects of the simulation.

Prior to their use in the simulation, synthetic populations are represented in data tables, often in a relational database or some equivalently structured file system. Typically there are separate tables for households and person records. The household records file provides details about various household-level socio-demographic attributes such as household income, size, number of workers, etc. Similarly, the person records file provides information about person-level attributes such as age, gender, employment status, etc. Person records are linked to household records through ID numbers.

TABLE 3-2 SAMPLE HOUSEHOLD RECORDS FILE

TAZ	HHID	Age of Household Head	Number of persons	Income Group	Presence of Children	Number Workers
143	16667	1	2	1	1	1
193	17392	1	2	4	0	1
77	232	1	3	1	1	2
18	5042	1	4	3	1	3

TABLE 3-3 SAMPLE PERSON RECORDS FILE

TAZ	HHID	Person ID	Age	Works from Home	Employment Status	Gender	Hours Worked per Week
77	232	1	22	1	1	2	9
77	232	2	24	1	0	1	45
77	232	3	1	0	1	2	0

Population synthesized by any synthetic population generator may be used with DaySim as long as the required household and person socio-demographic attributes are provided to it in the appropriate format. PopGen, the synthetic population generator developed at Arizona State University (ASU) was chosen for this effort primarily for two reasons. First, it has the ability to control for both household and person level demographic attributes simultaneously. Second, it has an easy-to-use and simple graphical user interface (GUI).

Preparing Synthetic Populations for DaySim

The design of the synthetic population should support the design of the activity-based model (DaySim in this case) and provide the variables it needs. In addition, the activity-based model should only rely on information that can be realistically provided in the synthetic population.

Population synthesis generally consists of the synthesis of two sub-populations – those living in regular households and those living in non-institutionalized group quarters such as college dormitories. For this effort, an additional segment of population was synthesized which comprised of seasonal households. These segments were established to reflect the differences in travel patterns associated with these sub-populations as well as to provide the ability to support seasonal analyses. For example, the seasonal population is generally older than the permanent population, has lower levels of workforce participation, and clusters in certain geographic areas. All of these attributes influence travel patterns and the demand for travel.

There are three major steps in creating a synthetic population:

1. Specifying the inputs to the process—the control variables and sample households as well as the level of geographic resolution. Specifying the control variables is essential. In addition, there is often an additional step of specifying additional, uncontrolled variables to be added to the synthetic population.
2. Actually running a program that produces the synthetic households.
3. The third major step would be transforming the model-generated outputs into characteristics of the population that will be used throughout the rest of the model system. This could involve creating categorical variables out of continuous variables, reformulating income, or allocating households from the zonal level to a finer level of geographic resolution, such as a parcel.

DaySim Person Types

Although person are being modeled in disaggregate form in an ABM, it is often useful to create person type categories. DaySim uses 8 such person types. Person type categories may be used for various purposes:

1. As a basic segmentation for certain models, such as daily activity pattern models
2. To summarize and compare observed versus estimated data and calibrate models
3. As explanatory variables in models
4. As constraints on alternatives that are available; for example, work and school activities are only available to workers and student; and driving is restricted by age

TABLE 3-4 DAYSIM PERSON TYPES

No.	Person Type	Age	Work Status	School Status
1	Full-time worker	18 or more	Full-time	None/Part-time
2	Part-time worker	18 or more	Part-time	None/Part-time
3	Retired person	65 or more	Unemployed
4	Non-working adult	Less than 65	Unemployed	None/Part-time
5	University student	18 or more	Unemployed/Part-time	Full-time
6	High school student	16 or more	Unemployed/Part-time	Full-time
7	Primary school child	5-15	Unemployed	Full-time
8	Preschool child	0-4	Unemployed	None

Control Attributes and Target Distributions

There are three major inputs required for population synthesis of which the first step is to identify a set of control attributes and their levels. Next, target distributions of the control attributes and their levels are derived at appropriate geographic units. These target distributions are also known as marginal control totals since they represent the margins of a joint distribution of multiple attributes. Typically, the smallest level of spatial resolution that can be feasibly and reliably used to control attributes is used. If control attribute totals are not accurate at a particular spatial unit, they could be specified at a lower resolution.

The following considerations are usually important in choosing control variables:

• The number of control variables is important. If there are too few, the synthetic population may not accurately reflect the true population. On the other hand, too many control attributes may cause sample issues. There may not be any sample households with joint attributes of the control variables and this could distort the synthetic population.
• Control attributes may be single or multi-dimensional. Multi-dimensional attributes can be treated as single dimensional attributes with number of categories equal to the product of the numbers of categories in individual attributes. The primary advantage of multi-dimensional attributes is more precise regional control over the correlation between attributes. The disadvantage again is with sparse sample.
• The best choices of variables, will be meaningful attributes that are somewhat “orthogonal” to each other, which means that their variance in the population is largely independent. Conversely, if there are two attributes that are highly correlated, then controlling for both may not achieve much more than controlling for just one.
• Finally, different sets of control attributes may be used for base and forecast years, if limited by forecasting accuracy. This is not necessarily desirable, though. The ability to forecast marginal control totals should be a consideration when specifying control attributes for this base year.

Target distributions of control variables for the base year could be obtained from a variety of data sources including the following:

• Decennial Census: ~100% sample
• American Community Survey (ACS) summary files: 3% sample, rolling 5-year sample, yields an estimate of ~15% of population
• Census Transportation Planning Products (CTPP)
• Other zonal data developed locally (TAZs)

For the forecast year, regional socio-economic forecasts or outputs from a land-use model are often used.

The following tables provide the list of control attributes and their levels along with the specific data sources used to obtain corresponding target distributions. All the distributions were obtained at the TAZ level.

TABLE 3-5 HOUSEHOLD CONTROL DATA FOR PERMANENT HOUSEHOLDS

Household Attribute	Category Number	Categories	Data Source
Householder unit type	1	Single family dwelling	NERPM TAZ Data
	2	Multi family dwelling
Presence of children	1	Yes	Census 2010 and ACS 2006-10
	2	No
Householder age	1	15 to 24 years	Census 2010 and ACS 2006-10
	2	25 to 54 years
	3	55 to 64 years
	4	65 to 74 years
	5	75 years and over
Household income (annual)	1	Less than $20,000	Census 2010 and ACS 2006-10
	2	$20,000 to $39,999
	3	$40,000 to $59,999
	4	$60,000 to $99,999
	5	$100,000 or more
Household size	1	1 person	Census 2010 and ACS 2006-10
	2	2 persons
	3	3 persons
	4	4 persons
	5	5 persons
	6	6 persons
	7	7 or more persons
Household size and workers joint	1	1 person, no worker	Census 2010 and ACS 2006-10
	2	1 person, 1 worker
	3	2 persons, no worker
	4	2 persons, 1 worker
	5	2 persons, 2 workers
	6	3 persons, no worker
	7	3 persons, 1 worker
	8	3 persons, 2 workers
	9	3 persons, 3 workers
	10	4 or more persons, no worker
	11	4 or more persons, 1 worker
	12	4 or more persons, 2 workers
	13	4 or more persons, 3 workers

TABLE 3-6 PERSON CONTROL DATA FOR PERMANENT HOUSEHOLDS

Person Attribute	Category Number	Categories	Data Source
Gender	1	Male	Census 2010 and ACS 2006-10
	2	Female
Age	1	Under 5 years	Census 2010 and ACS 2006-10
	2	5 to 14 years
	3	15 to 17 years
	4	18 to 24 years
	5	25 to 39 years
	6	40 to 54 years
	7	55 to 64 years
	8	65 to 74 years
	9	75 years and over

TABLE 3-7 HOUSEHOLD CONTROL DATA FOR SEASONAL HOUSEHOLDS

Household Attribute	Category Number	Categories	Data Source
Householder unit type	1	Single family dwelling	NHTS 2009 add-on survey for Florida
	2	Multi family dwelling
Presence of children	1	Yes	NHTS 2009 add-on survey for Florida
	2	No
Householder age	1	15 to 24 years	NHTS 2009 add-on survey for Florida
	2	25 to 54 years
	3	55 to 64 years
	4	65 to 74 years
	5	75 years and over
Household income (annual)	1	Less than $20,000	NHTS 2009 add-on survey for Florida
	2	$20,000 to $39,999
	3	$40,000 to $59,999
	4	$60,000 to $99,999
	5	$100,000 or more
Household size	1	1 person	NHTS 2009 add-on survey for Florida
	2	2 persons
	3	3 persons
	4	4 or more persons
Household size and workers joint	1	1 person, no worker	NHTS 2009 add-on survey for Florida
	2	1 person, 1 worker
	3	2 persons, no worker
	4	2 persons, 1 worker
	5	2 persons, 2 workers
	6	3 persons, no worker
	7	3 persons, 1 worker
	8	3 persons, 2 workers
	9	4 or more persons, no worker
	10	4 or more persons, 1 worker
	11	4 or more persons, 2 workers
	12	4 or more persons, 3 workers

TABLE 3-8 PERSON CONTROL DATA FOR SEASONAL HOUSEHOLDS

Person Attribute	Category Number	Categories	Data Source
Gender	1	Male	NHTS 2009 add-on survey for Florida
	2	Female
Age	1	0 to 17 years	NHTS 2009 add-on survey for Florida
	2	18 to 24 years
	3	25 to 39 years
	4	40 to 54 years
	5	55 to 64 years
	6	65 to 74 years
	7	75 years and over

TABLE 3-9 CONTROL DATA FOR GROUPQUARTERS RESIDENTS

Person Attribute	Category Number	Categories	Data Source
Gender	1	Male	Census 2010 and ACS 2006-10
	2	Female
Age	1	Under 18 years	Census 2010 and ACS 2006-10
	2	18 to 64 years
	3	65 years and over

Sample Data

During population synthesis, individual household and person records are drawn from a disaggregate sample of households to match target distributions of controlled attributes. It may not be possible to control all the desired attributes and so “uncontrolled” attributes are added to the synthetic population from disaggregate sample data. It is essential that the disaggregate sample is representative of the population of the entire region.

In most cases, the primary source of disaggregate sample data will is Public Use Microdata Sample (PUMS) data, which is now part of the ACS, and follows the same sampling framework, but provides disaggregate records for households and persons across numerous different attributes. PUMS is sampled and grouped according to geographic units, better known as PUMAs. PUMAs cover contiguous areas of roughly 100,000 population, including persons living in group quarters. For example, a metro area of 850,000 might be covered by 8 or more likely 9 PUMAs. In general, ACS-PUMS provides good representative coverage of most regions and is rigorously tested and monitored, so it is was used for creating sample data for this effort.

PopGen Run

The control totals and disaggregate sample data are input into a population synthesizer to generate a synthetic population. The joint distribution of the control attributes from the disaggregate sample is fitted to the control totals. This fitting or balancing is general done using the Iterative Proportional Fitting (IPF) algorithm or some variant of it which is at the core of most populations synthesizers. PopGen goes one step further in applying what is called an Iterative Proportional Updating (IPU) algorithm that not only matches household-level attribute totals but also person-level attribute controls simultaneously. The final step is then to draw individual household and person synthetic records from the disaggregate sample to match the fitted distribution.

Microzone / Parcel Allocation

As stated previously, DaySim operates at the parcel level. Since population synthesizers typically synthesize households at the TAZ level, synthetics households are then required to be assigned individual parcels or microzones. The process that does this need not be very complex. Currently, synthesized households of a particular TAZ are randomly assigned to all parcels within the TAZ based on parcel capacities. The parcel capacity or the number of housing units on a parcel is a required field in the base parcel file. The following steps are involved in the allocation process:

1. For all TAZs, parcel capacities are adjusted proportionately so that the total capacity obtained as a sum of capacities of all parcels within a TAZ is equal to the total number of households synthesized.
2. Each of the synthetic households within a TAZ is randomly located in one of the parcels within the TAZ.

The process is the same for group quarter population and this requires group quarter capacities for each parcel. In case there are other sub-populations like permanent and seasonal, they are all combined before the parcel allocation process. Currently, there exists an R-script the takes the synthetic population from PopGen as input and allocates households to individual parcels. The script also processes and recodes other necessary demographic attributes required by DaySim from sample data (generally Census/ACS PUMS) used in population synthesis. It then outputs a households and a persons file which can directly be read as inputs by DaySim.