1. Lec 16, LU Part 2: Scenarios & Assessment of transportation impacts on the urban activity system Scenarios Envision Utah’s scenarios Assessment of transportation impacts on the urban activity system

2. Major findings from regional workshops “Where do we grow?”“How do we grow?” Participants placed greater population in infill areas than in new expansion or new town areas  Favored more compact growth than sprawl Bar development above the benchline Viable agricultural lands should be preserved in the southern part of the region. Irrigated ones in the central and northern parts of the regions Rail and other non-automobile modes essential East-west transportation links needed (both road and rail) Waterways should be preserved as greenway and trail networks Minimize development on the Wasatch Back Participants favored the walkable development types. Emphasized the need for better east-west transportation connections (roads and transit), and indicated a general preference for transportation systems that balance the needs of the automobile with non-auto travel modes such as walking, bicycling, and transit use

3. Scenarios The use of future scenarios constructed on the basis of professional judgment, current or expected trends, and so forth. We discussed that mathematical models including simulation are not always “the” best methods. Scenario-based analyses are often viewed as being more hypothetical or conjectural in nature than model-based forecasts.  Actually the Envision Utah’s scenarios suffered from this view initially. Visit www.calthorpe.com then choose Project/Region/Envision Utah for more info about the Wasatch Front development issues.www.calthorpe.com Step 1: Identify key variables or attributes, then within that a set of criteria Step 2: Convene conferences or workshops of experts, citizen groups, etc. to describe the value ranges for the criteria identified in step 2. (Two regional workshops took place for the 4 scenarios of Envision Utah: “Where do we grow?” & “How do we grow?.” And many other local community workshops took place)

4. Attributes considered by Envision Utah Gateway value: Personal & Community Enrichment: Open space; Learning opportunity/school quality; Institutions that foster good values; Sense of community; Natural systems; Cultural opportunities Gateway value: Safe & Secure Environment: Crowding; Crime, Shared ideas; values & morals; Traffic safety/accidents Personal time & opportunity: Time- consuming activities; Leisure activity accessibility; personal health Financial security: Income levels; Affordable living; Taxation

5. Examples of attributes and criteria Crowding: Housing density Traffic congestion Population density Demands on recreational resources, etc Open space: Viewsheds % of HH within ½ mi of a park (10 min walk) % of HH within 30min of a recreational area Prime farm land Time-consuming activities: Avg. rush hour speeds Avg. trip time, length Total VMT Congested road miles Transit ridership & services Business & job opportunities: No. of jobs added to region Industries locating in region

6. Envision Utah’s 4 scenarios & impacts Scenario A: Auto-oriented sprawl (least controlled growth) Scenario B: Baseline (Still auto- oriented but with some plans)

7. Envision Utah’s 4 scenarios & impacts (cont.) Scenario C: Focus new development in walkable communities Scenario D: Focus new development in existing urban centers

8. Envision Utah’s 4 scenarios & impacts (cont.)

9. Two approaches for the assessment of transportation impacts (section 6.3) What has occurred which would not have occurred if the transportation system change had not been implemented? We are interested in a with and without comparison. But this is hard to do as the book explains Ex Post Assessment: Evaluate the current condition with the previous one.  Becomes a “before” and “after” comparison. With this we cannot answer the question above. A Priori Assessment: This assessment is focused directly at the with and without level of analysis.  The major problem with this is that it can estimated impacts only by using forecasted (rather than “real”) data. Good models are needed for this.