Oregon Statewide Integrated Model Title slide option 1 Overview of Version 2.5 OMSC Modeling Program Coordination Committee Presented by: Becky Knudson, Senior Transportation Economist Transportation Planning Analysis Unit, ODOT April 19, 2017

Outline New Features Peer Review Application SWIM 2.5

SWIM Schematic

Why 2.5… 4. SWIM assignment with VISUM 2. Refined Economic Module 5. Generalized Transit 2. Refined Economic Module 3. New Commercial Travel Mode 1. Replaced PI module with PECAS-AA

PECAS AA Production, Exchange, Consumption Allocation System - Activity Allocation Module Spatial input-output model Integrated representation of spatially distinct markets Represents how activities of households, industries, business and government locate Location determined by developer space Location responsive to how activity interacts with each other Pricing mechanism clears markets Steps through time

Model area forecasts for output and employment NED New Economics & Demographics Module Model area forecasts for output and employment Produces outputs by AA Activity, inputs & outputs, population in 5 yr age groups Uses DAS IHS-Global Insight forecast used for official revenue forecast

Generates freight flows for model area based on AA Module CT Commercial Travel Module Calibrated to traffic counts Generates freight flows for model area based on AA Module Uses FAF data and Rail Waybill

CT Original recipe

CT Updates in place Revise using FAF4 OD data and updated border truck counts

TS To VISUM Transport Supply (TS) replaced with VISUM Highway and transit skimming and assignment Microsimulated trip subarea analysis Networks already maintained in VISUM All spatial input data managed by VISUM (zonal, highway and transit) Simplification of transit network representation No longer managing local bus networks Modeling local bus LOS with local transit functions borrowed from the California STDM Various revisions to process requested by ODOT

Generalized Transit Calculates local transit in-vehicle time and out-of-vehicle time skims Based on California Statewide Travel Demand Model local transit functions (LTF) Builds transit connectors to premium network using LTF matrices The Transit Assignment process reads transit demand data created by the previously run SWIM modules and assigns it to the transit network managed in the version file. In the process, various skims are created (time, wait, cost, etc.), as well as assignment results and reports. The module does not have a local transit network. Instead, it calculates local transit in-vehicle time and out-of-vehicle time skims according to the California Statewide Travel Demand Model Local Transit Functions (LTF). This step also builds transit connectors to the premium network using the LTF in-vehicle time and out-of-vehicle time matrices from the zone centroid to the zone of the nearest X transit stops. Finally, it adjusts the intracity skims so only the skimmed skims or the LTF skims are available for an OD pair.

SWIM Small Test Bed for Changes to SWIM 20 zone version Procedures Parameters Settings Inputs Takes minutes to run instead of hours Big time saver

Applications and Peer Review On the same page because we did these simultaneously Applications and Peer Review

SWIM 2.5 Peer Review Frank Koppelman, Northwestern University, retired David Simmonds, Simmonds & Assoc, Cambridge England Michael Wegener, Univ. of Dortmund, Germany Keith Lawton, Oregon, formerly Metro Julie Dunbar, Dunbar & Associates Kim Fisher, Univ. Of Maryland, formerly TRB Bill Upton, Oregon, formerly ODOT SWIM 2.5 Peer Review

Panel Topics SWIM 2.5 performance Use for applications Refinement suggestions Lessons learned SWIM 2.5 Becky: ODOT wants this from Panel

Oregon’s Economy Relies on Freight Movement History of analysis: RRA1 arose from a question prompted by a Minnesota study… We used SWIM 2.0 and had no bridge deterioration model, was an exploratory analysis to understand the potential risk to the economy. RRA2 refines the analysis, using better tools with more information – yet a major reveal was the need for more data… We included a TAC to refine the outreach and messaging aspects. 2014 2017

RRA2 Detour

RRA2 Detour

Visualization Automation

SWIM TLUMIP https://github.com/tlumip/tlumip/wiki