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 organizationForecasting manager for Salt Lake City metropolitan planning organization MPOs maintain region’s short and long-term transportation plansMPOs maintain region’s short and long-term transportation plans The “3 C’s”The “3 C’s” Responsible for developing and using models to forecast future travel patternsResponsible for developing and using models to forecast future travel patterns Mathematical models representing current travel behavior are used to forecast future travel behaviorMathematical models representing current travel behavior are used to forecast future travel behavior Analyze future alternatives, quantify benefits and costsAnalyze 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 ComplexityComplexity Challenges (legal)Challenges (legal) CreativityCreativity 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: The 5 steps of modeling process (typically) are: 1. Land Use Forecasting 1. Land Use Forecasting 2. Trip Generation 2. Trip Generation 3. Trip Distribution 3. Trip Distribution 4. Mode Split 4. Mode Split 5. Trip Assignment 5. Trip Assignment

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

8. Networks

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

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

13. Trip-based Models

14. Limitations of Traditional Models Aggregate and Trip-basedAggregate and Trip-based Poor accountingPoor accounting Assume similarity within zonesAssume similarity within zones Over-simplifies family dynamics and location choiceOver-simplifies family dynamics and location choice No feedback to land-use forecasting processNo feedback to land-use forecasting process Land-use does not change with transportationLand-use does not change with transportation Simplistic response to land-useSimplistic response to land-use No sensitivity to urban form (diversity, density, design)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) Forecasts future land-use (households, jobs) Effective means to incorporate city and county land-use plans into regional transportation plans Effective means to incorporate city and county land-use plans into regional transportation plans State-of-the-art State-of-the-art Defensible microeconomic theory Defensible microeconomic theory Incorporates transportation accessibility Incorporates transportation accessibility Locally calibrated Locally calibrated Tremendous interest across the U.S. Tremendous interest across the U.S.

18. WFRC Interest Committed to exploring and discussing linkages between land-use and transportation in LRTP 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 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 UrbanSimTravel Models Households by Income Age of head Size Workers Children Employment by sector Accessibility Highway Travel Times Vehicle Ownership Probabilities

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

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

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

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

29. Observed Predicted Land Price Validation

30. Residential Location Validation Observed TotalObserved %Modeled Utility

31. Visioning Plans to test UrbanSim extensively over next 4-6 months Plans to test UrbanSim extensively over next 4-6 months Plenty of opportunity for local review and feedback Plenty of opportunity for local review and feedback Relatively safe opportunity to vary land and transportation policies and see what the model says 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 Political issues can be more challenging than the technical Inherent resistance to change Inherent resistance to change Committing to a tool like UrbanSim affects entire planning realm (local/regional/state) Committing to a tool like UrbanSim affects entire planning realm (local/regional/state) Implications for project development must be well understood Implications for project development must be well understood