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

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

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

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

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)

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?

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)

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

MSA – Impact on Convergence

MSA – Impact on Convergence

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.)

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)

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

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.)

Contacts Chrissy Bernardo Peter Vovsha Technical Principal Systems Analysis Group Chrissy.Bernardo@wsp.com Peter Vovsha Assistant Vice President Peter.Vovsha@wsp.com