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

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

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

Synthesizing an Aging Population …playing God.

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

Aging Population Assumption 30 percent increase in 1 & 2 person households with at least 1 65+ 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

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?

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 60-65 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…

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