Modeling in the “Real World”

1. Modeling in the “Real World” John Britting Wasatch Front Regional Council April 19, 2005

2. Introduction • Forecasting manager for Salt Lake City metropolitan planning organization • MPOs maintain region’s short and long-term transportation plans • The “3 C’s” • Responsible for developing and using models to forecast future travel patterns • Mathematical models representing current travel behavior are used to forecast future travel behavior • Analyze future alternatives, quantify benefits and costs

3. Quick Facts • 2 MPOs • 4 Counties • 1300 Square Miles • 1.8 million people today • 2.7 million people by 2030

4. Typical Analyses 1) Air Quality Conformity -NAAQS 2) System Performance (aggregate) -VMT, VHT, Mode Share, etc. 3) Corridor-level Analyses -Identify and compare options 4) Facility Performance -V/C, Ridership, speed

5. The other 3 C’s • Complexity • Challenges (legal) • Creativity Advancing the modeling practice is not easy.

6. What is a Travel Model? A systematic tool to forecast future travel. One of many tools used in decision-making process. • The 5 steps of modeling process (typically) are: • 1. Land Use Forecasting • 2. Trip Generation • 3. Trip Distribution • 4. Mode Split • 5. Trip Assignment

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

8. Networks

9. Trip Generation Each zone produces and attracts trips, based on the amount and types of activities within the TAZ. • Trip Generation • Trip Distribution • Mode Choice • Trip Assignment LANDUSE DATA TAZ Population Jobs 1000 0 393 500 300 679 0 800 176 Modeling Steps

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

11. 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

12. 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

13. Trip-based Models

14. Limitations of Traditional Models • Aggregate and Trip-based • Poor accounting • Assume similarity within zones • Over-simplifies family dynamics and location choice • No feedback to land-use forecasting process • Land-use does not change with transportation • Simplistic response to land-use • No sensitivity to urban form (diversity, density, design)

15. Tour-based Models

16. 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)

17. 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.

18. WFRC Interest • Committed to exploring and discussing linkages between land-use and transportation in LRTP • Wasatch Choices visioning effort • Extensive staff time fine-tuning UrbanSim database and model • Major technical questions have been answered • Testing about to begin anew in visioning effort

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

20. Linked Urban Markets Services Governments Infrastructure Land Housing Developers Floorspace Households Labor Businesses Flow of consumption from supplier to consumer Regulation or Pricing

26. 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)

27. 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)

28. 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

29. Land Price Validation Predicted Observed

30. Residential Location Validation Observed Total Observed % Modeled Utility

31. Visioning • Plans to test UrbanSim extensively over next 4-6 months • Plenty of opportunity for local review and feedback • Relatively safe opportunity to vary land and transportation policies and see what the model says

32. 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) • Implications for project development must be well understood