1. 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

2. 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

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

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

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

6. 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

7. 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

8. 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

9. 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.

10. 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

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

12. 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.

13. 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.

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

15. 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.

16. Brownfield Redevelopment Site
Port Covington

17. 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

18. 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

19. 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

20. 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

21. 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?

22. Highway Assignment – VMT Change from Base

23. 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

24. For More Information Yijing Lu: ylu@baltometro.org
Charles Baber:
David Kurth:
Thomas Rossi: