1. Incorporating Greenhouse Gas Considerations in RTP Modeling Jerry Walters, Fehr & Peers CTC Work Group Meeting on RTP Guidelines June 28, 2007

2. Linkages between RTP and GHG Land Use and Transportation Policies Land Use Transportation Nets Built Environment TDM Vehicle Miles Vehicle Trips Vehicle Speeds CO 2 Emissions Other GHG Global Warming

3. Linkage 1. 4D Relationships between Travel and Built Environment Land Use and Transportation Policies Built Environment Vehicle Miles Vehicle Trips CO 2 Emissions

4. Land Use and Transportation Policies Land Use Vehicle Miles Vehicle Trips CO 2 Emissions Linkage 2: Induced Investment, Development, Travel

5. Land Use and Transportation Policies Transportation Nets TDM Vehicle Miles Vehicle Trips Vehicle Speeds CO 2 Emissions Linkage 3: Mobility Return on Investment

6. Linkage 1: 4D Relationships between Travel and Built Environment

8. Variation in VMT compared to Trend Scenario

9. Trip generation is directly related to D’s: Density dwellings, jobs per acre Diversity mix of housing, jobs, retail Design network connectivity Destinations regional accessibility Distance to Transit rail proximity

10. Shortens trip lengths More walking/biking Supports quality transit Density (jobs and dwellings per acre)

11. Links trips, shortens distances More walking/ biking Allows shared parking Diversity (mix of housing, jobs, retail)

12. Design (connectivity, walkability)

13. Destinations (accessibility to regional activities) Development at infill or close-in locations reduces vehicle trips and miles

14. Transit shares higher within ¼ mile and ½ mile of station Distance to Transit

15. Vehicle Trips Per Capita VMT per Capita Density4% to 12%1% to 17% Diversity1% to 11%1% to 13% Design2% to 5%2% to 13% Destinations5% to 29%20% to 51% 4D Elasticity Ranges Sources: National Syntheses, Twin Cities, Sacramento, Holtzclaw

16. Land Use Clustering, Mixing, Traditional Neighborhood Design – All Reduce Travel Why it matters: 55% to 65% of trips are less than 3 miles. Up to 80% are less than 5 miles.

17. Shortcomings of Conventional Travel Models in Assessing Smart Growth Primary use is to forecast long-distance auto travel on freeways and major roadsPrimary use is to forecast long-distance auto travel on freeways and major roads Secondary use is to forecast system-level transit useSecondary use is to forecast system-level transit use Short-distance travel, local roads, non-motorized travel modes are not addressed in model validationShort-distance travel, local roads, non-motorized travel modes are not addressed in model validation

18. Typical Model “Blind Spots” Abstract consideration of distances between land uses within a given TAZ or among neighboring TAZ’s Limited or no consideration intra-zonal or neighbor- zone transit connections Network in Model Network in Field

19. Typical Model “Blind Spots” Sidewalk completeness, route directness, block size generally not considered.Sidewalk completeness, route directness, block size generally not considered.

20. Typical Model “Blind Spots” Little consideration is given to spatial relationship between land uses within a given TAZ (density)Little consideration is given to spatial relationship between land uses within a given TAZ (density) Interactions between different non-residential land uses (e.g. offices and restaurants) not well representedInteractions between different non-residential land uses (e.g. offices and restaurants) not well represented

21. Potential Sources of Solutions Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies (Caltrans) Urban Development, VMT and CO 2 Emissions, (Smart Growth America) Smart Growth INDEX (EPA) Travel Characteristics of TOD in California (Caltrans/ Lund, Cervero, Willson)

22. Caltrans Study Conclusions Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies

23. Caltrans Study Recommendation Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies Use 4D’s to compensate for any lack of sensitivity in presiding model.

24. 2 Induced Investment Development, Travel

25. Average VMT Elasticities to Added Capacity Facility-Specific Studies Areawide Studies Short-Term00.4 Medium-Term0.27NA Long-Term0.630.73

26. Integrated Land Use/ Transportation Models PECAS Users: Sacramento SACOG, Caltrans, SANDAG (considering), Ohio DOT, Baltimore MPOPECAS Users: Sacramento SACOG, Caltrans, SANDAG (considering), Ohio DOT, Baltimore MPO URBANSIM Users: Salt Lake, Seattle, Houston, Honolulu, DetroitURBANSIM Users: Salt Lake, Seattle, Houston, Honolulu, Detroit UPLAN Users: Merced, WilmingtonUPLAN Users: Merced, Wilmington What-If Users: FresnoWhat-If Users: Fresno

27. Cautionary Notes on PECAS, URBANSIM Both are data intensive Both require significant staff and/or consultant support to implement, use, maintain Both require calibration and extensive model development Validation experience very limited

28. 3 Mobility ROI

29. Investment in System Continuity

30. Q&A