1. ABM SENSITIVITY TESTING IN THE BALTIMORE REGION – WITH BABY-BOOMER RETIREMENT, WHO’S FILLING THE JOBS IN THE FUTURE?
2017 Transportation Planning Applications Conference
David Kurth (CS)
Yijing Lu (BMC)
Matt de Rouville (BMC)
Charles Baber (BMC)
Tom Rossi (CS)
Our presentation in the 8:30 session this morning, ACTIVITY-BASED MODEL SENSITIVITY TESTING – SHOWING THE MODEL’S SENSITIVE SIDE, provided a number of details regarding this sensitivity test.
The purpose of this presentation is to present some thoughts on the inputs, specifically the synthesized population, to the aging scenario sensitivity test and our responsibility to review these for reasonableness.
May 16, 2017

2. Benefits…and Liabilities…of ABMs
Forecasts traveler behavior
Increased sensitivity
Complex variable interactions
Liabilities
Increased sensitivities
Complex variable interactions
Need careful check of input and output reasonableness
Activity based models offer tremendous benefits for travel forecasting
Provide insights into traveler behavior rather than simple travel patterns
Provide increased sensitivity to what is changing in the real world:
Population demographics
Transportation supply
Capture interactions of variables
There are associated liabilities
Models are typically based on snapshot data so increased sensitivity may not be truly reflective of traveler behavior over time
Sometimes hard to understand variable interactions – they may be illogical
Since there are many inputs and interactions, it’s increasingly important to check reasonableness of input variables

3. Synthesizing an Aging Population
InSite Sensitivity Test – “What happens to travel as the population ages?”
Reasonable input population is key
Reasonable has many facets
This test underscores the difficulty with…
The many facets of “reasonable”
Population synthesizers, PopGen2 for BMC, require a number of marginal distributions to control the output. For BMC:
At the County level:
Households by workers and income (20 categories, 4 workers * 5 income group)
Persons by age and sex (36 categories, 18 age group * 2 gender group)
Group quarters population by total number of group quarters
At the TAZ level:
Households by household size (5 categories), income group (5 categories)
Persons by employment status (employed and unemployed)
Group quarters population by number of institutional group quarters and the number of non-institutional group quarters
Once matching the marginals, the synthesizers sample a base population (e.g. ACS data) for other household and person characteristics required by the model
These are uncontrolled and may not actually match desired distributions or bi-variate distributions
They also reflect a underlying base population that may change in the future

4. Synthesizing an Aging Population
…playing God.

5. Change in Proportion of Population by Age Group 2010 to 2030 – Maryland
0-19
21% 2%
20-64 8%
-10%
65+
72%
45%
This is the change in the proportions (percents) of population in Maryland between 2010 and 2030
Derived from US Census population pyramids for the two years
Of course, populations in each age group may increase even though the percent of the total population decreases
In particular in this summary, while populations in the aggregate child, prime working age, and senior groups increase, the percent of the total population in the prime working age group decreases. As the economy continues to grow, who will make up this slack – robots or seniors?
Source: Cambridge Systematics Summary of US Bureau of Census Data

6. Aging Population Assumption
30 percent increase in
1 & 2 person households with at least person
Employment kept constant with base scenario
This summarizes the assumptions made for the Aging Population Scenario tests for the BMC region…
Impact (decrease) on Full- and Part-time workers obvious
Full- and Part-time worker person types are not restricted to <65; 65+ can still be classified a worker if that’s the designation in the synthesized population
Seniors are “non-workers” age 65+
Non-working adults are age 16+ and not a student

7. Change in Work Tours Overall, a 5.5% decrease in total work tours
The impact on the number of work tours is obvious:
Tours by full-time workers decreased by >132,000 or 7%
Tours by part-time workers decreased slightly ~1000 or 0.7%
Tours by non-working adults and seniors increased slightly
50 for NWA => ~6%
8800 for Seniors => 53%
Key here is that the ABM does not preclude work tours by these groups
However, probably a result of volunteer work or casual employment
Since employment kept the same in this scenario, the implication is that 5.5% of the jobs in the region are unfilled
Is this reasonable, or do we somehow need to ensure that more of the “synthesized” 65+ population remains in the work force either as full- or part-time workers?
Overall, a 5.5% decrease in total work tours
5.5% of available jobs not being filled?

8. Modeling Considerations
Synthetic Population
Marginals Consistency
Household Workers
Household Income
Person age
Source data reflects past, not the future
If 70 = new 65:
Sample characteristics of households to increase working 65+
Modeling
65+ should have ability to make work tours
Avoid arbitrary categories for model parameters
“Seniors” might be better defined as 70+ in future rather than 65+
There are many considerations…
…and these just underscore the admonition…

9. Modeling Considerations
“It's tough to make predictions, especially about the future.” (Yogi Berra)