Activity-Based Model Sensitivity Testing – Showing the Model’s Sensitive Side Yijing Lu (Baltimore Metropolitan Council) David Kurth (Cambridge Systematics) Charles Baber (Baltimore Metropolitan Council) Matt de Rouville (Baltimore Metropolitan Council) Thomas Rossi (Cambridge Systematics) Click to add date

Model Region (2012) 10 Jurisdictions (6 BMC/4 MWCOG) 2793 Traffic Analysis Zones (TAZ) 5+ Million Persons 2+ Million Households 3+ Million Jobs 13+ Million Daily Trips

Carroll Harford Frederick Baltimore County Howard Anne Arundel Montgomery Baltimore City Prince George’s Washington, DC

Truck and Special Generator Static Traffic Assignment InSITE Model System PopGen 2.0 InSITE TourCast Truck and Special Generator Static Traffic Assignment

Transportation Networks Model Scenarios Scenario Synthetic Population Land Use Pattern Transportation Networks 2012 Base Year Base Aging Population Modified Brownfield Redevelopment Freeway Capacity Increase

Aging Population Scenario Changes from Base Scenario 30 percent increase in: 1 & 2 person households with at least one person in retirement age (65+) Total population & households unchanged Employment levels unchanged

Aging Population– Change in Persons by Type   Age Range Population Person Type 0-17 18-19 20-64 65+ Total Children 0% 7% – 207 Adult Student 6% -3% 44% 1,674 -753 Full Time Worker 4% -9% 26% -7% 23,620 -168,378 Part Time Worker -2% -6% 29% -1% 12,983 -3,903 Non-working Adult 5% -8% -46,850 Senior 47% 218,587 42% 256,864 -1,090 Sum of Adults <65 (Adult Students, Full-time workers, Part-time workers, & Non-working adults) = 219,884 Lost 1,090 people due to vagaries of PopGen2 – this is 0.02% of the total population Target was 30% increase in 1 & 2 person households with at least one 65+ person Resulted in 42% increase in 65+ persons Resulted in 47% increase in “Seniors” (i.e. non-working) This is due to household sampling of PUMS ACS data – 2+ person households with one 65+ person more likely to have two 65+ persons

Aging Population– Daily Activity Patterns As expected: a decrease in full- & part-time workers with work tours An increase in non-work tours and staying at home by seniors

Aging Population– Tour Destination Choice The aging population scenario had almost no impact on average work tour durations. This was as expected: There was no systematic geographic clustering of aged households There was no change in the employment forecast.

Aging Population– Work Tour Destinations -6.4% -7.7% -4.5% -6.1% -6.9% -6.1% -6.4% -3.4% Overall, there was a 5.5 percent decrease in Work Tour Destinations per Employee. The implication is that these are unfilled jobs. -6.1% -4.8 Change in Work Tour Destinations per Employee

Aging Population– Tour Time of Day (Work) The tour arrival times at work show similar patterns with, of course, fewer overall work tours.

Aging Population– Tour Time of Day (Work) This slide shows the “Percent Change from Base scenario by Arrival Time Period.” While the overall patterns shown in the previous slide were similar, there was, in fact, a time-of-day impact of the aging population. The red dashed line is the total daily percent difference in work tours–a decrease of a little more than 6%. The solid blue line shows the percent decrease in work tours by time period. Below the red dashed line implies that relatively fewer workers than the average change are arriving during the time period. In effect, this shows that the it’s a later arriving crowd – or that “normal” 8-5 workers are impacted more by the aging scenario.

Aging Population– Tour Time of Day (Other) So, what are those new seniors doing during the day? As expected, they’re making non-mandatory tours, and… They’re making them in the middle of the day. There was a 3.6% increase in non-mandatory tours over the day as shown by the red dashed line. From 7:30 AM to 5:00 PM, relatively more non-mandatory tours were made. From 5:00 PM to 7:30 AM, relatively fewer non-mandatory tours were made.

Aging Population– Tour Mode Choice Work Tours Non-Work Tours Overall, there was very little change in mode choice.

Brownfield Redevelopment Scenario Changes from Base Scenario Move from BMC suburban areas to brownfield area : 13,700 employees 4,700 households 12,000 residents “Simple” TAZ moves PopGen2 not rerun Regional population & employment was unchanged Unlike Aging Scenario, PopGen2 was not rerun. Households (including their associated members) and employment was simply moved from other TAZs to the 2 TAZs comprising the Brownfield site.

Brownfield Redevelopment Site Port Covington

Brownfield Scenario – Workplace Location 100 380 3600 40 360 150 8100 2400 Increase from Base Scenario in workers with regular workplace in Brownfield TAZs The locations of workers choosing the Brownfield site as shown above appears to be logical…most come from locations with good access to the site: Baltimore City Baltimore County Anne Arundel County But… 300 30

Brownfield Scenario – Workplace Location Workers Choosing Location as Regular Workplace per Employee Assessment Workers / employee at site unreasonably high Workplace location choice is not doubly constrained Larger area rates reasonable Investigate refinement of model

Brownfield Scenario Impacts on Other Results Very little regionwide impact on other models in comparison to Base Scenario Localized impacts: Increase in transit ridership Decrease in roadway levels of service

Freeway Capacity Increase Scenario Changes from Base Scenario Increase per lane capacity by 10 percent on Baltimore Beltway between Harrisburg Expressway and I-95 No changes to population and employment data Regional population & employment was unchanged

Freeway Capacity Increase Scenario Impacts Changes from Base Scenario No noticeable impact on: Workplace location choice Tour destination choice Tour time-of-day choice Transit assignment Minimal impact on: Tour mode choice for auto modes No discernable patterns Result of “random noise” from static equilibrium assignment?

Highway Assignment – VMT Change from Base

Summary InSITE reasonably sensitive Regional-level demographic changes All model components affected Noticeable travel demand changes Localized land use changes Most model components affected Impacts most noticeable near land use change Localized network changes Little impact on travel demand Localized assignment impacts Ability to check intermediate results

For More Information Yijing Lu: ylu@baltometro.org Charles Baber: cbaber@baltometro.org David Kurth: dkurth@camsys.com Thomas Rossi: trossi@camsys.com