Modeling in Support of Long Range Transportation Planning Scott Festin, AICP Data Manager Wasatch Front Regional Council March 29, 2007

Introduction 11 years at WFRC –Data Manager Socioeconomic Data GIS BS, Geography, University of Utah MA, Geography, UNC-Charlotte Eisenhower Research Fellow, Federal Highway Administration, Washington, DC

Wasatch Front Regional Council Metropolitan Planning Organization (MPO) for the Salt Lake City and Ogden-Layton Urbanized Areas 46 Cities, 3 Counties in Urbanized Area Additional work in Tooele and Morgan Counties “3C” process –Comprehensive –Coordinated –Continuing Coordinate with Mountainland Association of Governments (Provo/Orem Urbanized Area)

Wasatch Front Regional Council 18 Member board of elected officials –Salt Lake County-8 members –Davis County-4 members –Weber County-4 members –Morgan County-1 member –Tooele County-1 member –3 Non-Voting members from: Utah Association of Counties Utah League of Cities & Towns Envision Utah Professional Staff of 22

WFRC Staff Chuck Chappell, Executive Director –9 Planners –7 Engineers –Support staff Short Range PlanningTIP Long Range PlanningRTP ModelingTravel Model/UrbanSim Technical SupportSocioeconomics, GIS, Air Quality AdministrationAccounting, Secretarial, Public Involvement, CDBG, Etc.

Wasatch Front Regional Council Metropolitan Planning Organization (MPO) for the Salt Lake City and Ogden-Layton Urbanized Areas Federally mandated and created by the Governor. Urbanized areas with more than 50,000 population required to have an MPO. (23 USC 134 (b)(1)) WFRC formed in 1969, made an MPO in Two primary products –Regional Transportation Plan (RTP) –Transportation Improvement Program (TIP)

Regional Transportation Plan Must contain the following elements: –An identification of transportation facilities that should function as an integrated metropolitan transportation system. –A financial plan based on reasonably anticipated revenues that demonstrates how the adopted long- range transportation plan can be implemented. –Ensure the preservation of the existing metropolitan transportation system, and make the most efficient use of existing transportation facilities –Indicate proposed transportation enhancement activities Must be updated at least every four years Minimum 20 year planning horizon

Transportation Improvement Program Must contain the following elements: –a priority list of proposed federally supported projects and strategies to be carried out within the 4-year period after the initial adoption of the transportation improvement program; and –a financial plan that: (i) demonstrates how the transportation improvement program can be implemented; (ii) indicates resources from public and private sources that are reasonably expected to be available to carry out the program; (iii) identifies innovative financing techniques to finance projects, programs, and strategies; and (iv) may include, for illustrative purposes, additional projects that would be included in the approved transportation improvement program if reasonable additional resources beyond those identified in the financial plan were available.

Funding Prioritization All Federal Transportation funding must be prioritized and authorized by the Board. This is done through the TIP and RTP. –Projects need to be on the RTP and TIP to be built. In the first phase of the RTP ( ), there will be approximately $5.7 Billion in highway and transit capital expenditures.

Modeling to Support Transportation Planning Socioeconomics Travel Demand Modeling Air Quality Modeling UrbanSim Landuse Model Real World Data

Socioeconomic data form the foundation of all MPO modeling WFRC maintains the only set of small area population and employment projections –Internal uses –Public sector uses Water and sewer planning Schools –Private sector uses Economic development Community activism Socioeconomics Socioeconomic Data Travel Demand Model Air Quality Model

What is a Travel Model? A systematic tool to forecast future travel. One of many tools used in decision-making process. The 4 steps of modeling process (typically) are: –0. Land Use Forecasting (Socioeconomics) –1. Trip Generation –2. Trip Distribution –3. Mode Split –4. Trip Assignment

Model Inputs Network of zones and links 1300 zones contain socioeconomic data (people/jobs) 20,000 links describe road/transit infrastructure (lanes, speed, capacity, headway etc.)

Networks

Trip Generation Trip Distribution Mode Choice Trip Assignment Each zone produces and attracts trips, based on the amount and types of activities within the TAZ. Modeling Steps TAZ PopulationJobs LANDUSE DATA

Trip Distribution Trip Distribution estimates the number of trips between zones Trip Generation Trip Distribution Mode Choice Trip Assignment Modeling Steps

Mode Choice Mode Choice considers travel time, auto availability, and costs in estimating the likelihood of making trips by car, train, bus, etc. Trip Generation Trip Distribution Mode Choice Trip Assignment Modeling Steps

Trip Assignment Trip assignment estimates which road or route should be taken. Considers travel time, congestion, speed, distance, transit transfers, etc. Trip Generation Trip Distribution Mode Choice Trip Assignment Modeling Steps

Difficult Emerging Questions Land-use affects transportation decisions Transportation affects land-use growth New technologies (e.g. ITS, rail) New policies (e.g. tolls, taxes)

Introduction to UrbanSim Forecasts future land-use (households, jobs) Effective means to incorporate city and county land-use plans into regional transportation plans State-of-the-art –Defensible microeconomic theory –Incorporates transportation accessibility –Locally calibrated –Tremendous interest across the U.S. Still in developmental stages First use for planning was past summer

WFRC Interest Effort began in 1997 Committed to exploring and discussing linkages between land-use and transportation in RTP –Wasatch Choices visioning effort Extensive staff time fine-tuning UrbanSim database and model –Major technical questions have been answered –Updating input data

UrbanSim – Travel Model Interactions UrbanSimTravel Models Households by Income Age of head Size Workers Children Employment by sector Accessibility Highway Travel Times Vehicle Ownership Probabilities

Linked Urban Markets Governments Infrastructure Land FloorspaceHousing HouseholdsBusinessesLabor Services Developers Flow of consumption from supplier to consumer Regulation or Pricing

Overview of Modeling system >30 models within local UrbanSim application –Land Value (by type of use) –Real Estate Development (by type of use; intensity) –Residential location (by type of household) –Employment location (by type of industry)

Key Variables in Models Land value Vacant land (for developer models) Accessibility measures (for example) –Proximity to transportation facilities –Jobs/households within 30 minutes Neighborhood traits (for example) –Housing and employment within walking distance –Neighborhood mix (e.g. by income, by type of real estate) Decision-maker’s characteristics (e.g. income, HH size, sector)

Model Constraints Environmental features –Steep slope –Wetlands/lakes/streams –Superfund Regional Policies –Urban growth boundary –Open Space Local Land Policies –Type of use –Allowable density of use

Political Challenges Political issues can be more challenging than the technical Inherent resistance to change Committing to a tool like UrbanSim affects entire planning realm (local/regional/state)

Real World Data Models are a representation of reality –There is some uncertainty in all models –Law of diminishing returns Models are only as good as the data inputs –“Garbage in, Garbage out"

Data Sources Very little data used in modeling was collected for use in modeling UrbanSim input data –Parcel data collected and maintained by County Assessors for Property tax assessment. –Employment data collected by Workforce Services for administering unemployment insurance. Data limitations

Best Available Data The data you want may not always available Data that you can make work, is usually available Make due with the data that’s available –“Use it up, wear it out, make it do or do without.“ -FDR Data are surprisingly flexible. They can be made to fit.

Data Synthesis Sometimes you have to be creative in getting the data you need. Example: –Commercial space in Davis County Not in parcel data Estimates of floor space per employee Have locations of employers and number of employees Create space by calculating space requirements by number of employees It's an estimate and it's not perfect.

Emerging Issues American Community Survey –Census Long-Form data on an annual basis –Trade-offs Disaggregated models vs. Data privacy –Models are working at a finer level –Many data sources have privacy restrictions

Questions Contact Information –Scott Festin, AICP Wasatch Front Regional Council 295 Jimmy Doolittle Rd. Salt Lake City, UT (801) x113