1. Practical Innovations and Experience in ABM Equilibration with Network Assignments
Chrissy Bernardo, Peter Vovsha, Gaurav Vyas (WSP),
Rebekah Anderson & Greg Giaimo (ODOT)

2. Background
3C ABM
Concurrent ABM development for 3 major metropolitan areas in Ohio:

3. ABM Equilibration Global Feedback
Land Use
Network Level of Service
Accessibilities and O/D Measures
Travel Demand Locations, Modes, and Tour Structure
O/D Travel Patterns by Mode
Travel Paths by Mode
Global Feedback
Equilibrium between supply and demand
Convergence ensures uniqueness of model output for each scenario

4. Equilibrium criteria = compromise between benefits and costs
Global Equilibration
Benefits
Costs
Uniqueness/replicability of solution
Equilibrium between supply and demand
More accurate/stable results
Equilibrium criteria = compromise between benefits and costs

5. Strategic Feedback Options
4-Step Models
Averaging trip tables
Averaging skims
Averaging link volumes
Averaging speeds
Re-run mode choice only
Skip transit assignment if no major impacts expected on transit modes
Activity-Based Models
All 4-step options, plus:
Re-run non-mandatory location choice / tour formation, time of day, & mode choice only
Ramp-up demand (sampling)

6. Equilibration Strategies: MSA
MSA (method of successive averages)
Simple form:
𝑥 𝑛 = (𝑛−1) 𝑛 𝑥 𝑛−1 + 𝑥 𝑛 𝑛
Essentially keeps a running average, cutting down on “noise” / oscillation introduced in each iteration  speeds up convergence
Simple concept, but to what data items should you apply it?

7. MSA Dimensions: Hierarchy
LOS Feedback:
Zonal Skims
Link Speeds
Link Flows
Output / Drivers
Non-linear impact (path choice)
Direct Demand Feedback:
Trip Tables
(for static assignment only) +
Non-linear impact (BPR)

8. Convergence Analysis
Ran a full set of global iterations for Lima, OH (3C ABM)
100% population
All steps run on each iteration
Transit Assignment (with feeding back dwell times based on ridership)
Accessibilities (impacts both long-term choices and activity participation)
Used complete cold start (free flow speeds)
Two MSA options:
MSA on link volumes only
MSA on link volumes and trip tables

9. MSA – Impact on Convergence

10. MSA – Impact on Convergence

11. Additional Equilibration Options
Run time savings
Finer control over model sensitivity
Good for analyzing temporary projects or short-term impacts
Good for analyzing minor projects that are not expected to impact long-term choices
Re-run selected ABM Steps
Potential options:
Re-run mode choice only (3C)
Re-run non-mandatory location choice / tour
formation, time of day, & mode choice only
Projects that are temporary or very near-term may not affect long-term choices  this option allows the model to evaluate immediate impacts
Re-running just mode choice & assignment does not capture how people may re-structure their activities (e.g. go shopping on the way home from work instead of making a separate tour, go out to dinner near workplace instead of near home, etc.)

12. Additional Equilibration Options
Run time savings
Good for major network changes or future years
Ramp-up demand
Sample successively larger proportions of the population to run through the ABM
Each iteration, a percentage of households are randomly sampled across the entire region to run through the ABM
Final iteration will always use 100%
Each household is weighted as necessary to represent the full population of travelers  20 to 30% sample provides enough spatial coverage to represent network LOS
Speeds up cold start convergence, allowing demand to respond to changed conditions in early iterations
Big changes to inputs may require more iterations to converge, this speeds up each global iteration
Cannot be combined with other options (like only re-running selected ABM steps)

13. Run Time Savings – Estimates for Columbus
Feedback Strategy
Time Saved:
1st of 3 Global Iterations
Overall Time Saved:
3 Global Iterations
Impact on Model Results
MSA over Link Volumes --
Faster convergence
MSA over Trip Tables
Up to 1 global iteration
None
Ramp-up Demand
60% ABM run time
30% ABM run time
Negligible
Re-run Mode Choice Only
50% ABM run time
Change in Modes Only
Re-run Tour Formation + Only
40% ABM run time
Changes in tour structure and non-mand. locations

14. Run Time Savings – Estimates for Columbus
Feedback Strategy
Impact on Model Results
Recommended for:
Example Project/Run
MSA over Link Volumes
Faster conv.
All model runs
Any
MSA over Trip Tables
Ramp-up Demand
Negligible
Major changes to inputs
Future Year
Re-run Mode Choice Only
Change in Modes only
Capturing short-term impacts
New transit service (FTA approach)
Re-run Tour Formation + Only
Changes in tour structure and non-mand. locations
Capturing medium-term impacts (commuting patterns stay the same but other behavior may shift)
Autonomous vehicles, major temporary network changes (e.g. highway closure, bridge collapse, rail line closure, etc.)

15. Contacts Chrissy Bernardo Peter Vovsha Technical Principal
Systems Analysis Group
Peter Vovsha
Assistant Vice President