Wu Sun Joaquin Ortega Rick Curry San Diego Association of Governments May 18 th , 2009

1. Impact of Transportation Demand Management Policies on Green House Gas Emissions: A Modeling Approach Wu Sun Joaquin Ortega Rick Curry San Diego Association of Governments May 18th, 2009 12th TRB Transportation Planning Application Conference

2. Introduction • Energy and climate change are on top of federal, state and local government’s political and economic agenda • California Global Warming Solutions Act of 2006 (AB 32): reduce greenhouse gas (GHG) emissions to 1990 levels by 2020. • California Senate Bill 375: Air Resources Board (ARB) will establish mandatory reduction targets for GHG emissions from passenger cars and light-trucks for years 2020 and 2035. • California Assembly Bill 1493: enforced California vehicle emission standards, allows California to enact and enforce emission standards to reduce GHG emissions. • Low Carbon Fuel Standard (LCFS): requires fuel providers to reduce the carbon intensity of transportation fuels sold in the state by 10% by 2020

3. Introduction (Continued)

4. Introduction (Continued) • San Diego County emitted 34 MMT of CO2 in 2006, compared with 29 MMT in 1990, an 18% increase over 1990 levels • By 2020, under a business-as-usual scenario, regional GHG emissions are expected to be 43 MMT CO2. • In 2006, emissions from transportation sector represented 46% of total GHG emissions in San Diego County.

5. Introduction (Continued)

6. SANDAG Travel Forecasting Model • 4-step model • 4605 zones • 3 time of day periods • 10 trip purposes • nested mode choice structure • trip generation depends on land use inputs • TransCAD platform • Activity-based model (ABM) • Disaggregate • Behaviorally realistic • Household socio-demographic attributes

7. Emission Inventories Calculation for Vehicles • Emfac2007: the latest emission inventory model developed by the California Air Resources Board (CARB) • SANDAG 4-step model produce an input file to Emfac2007

8. Modeled GHG Reduction Strategies • Low Carbon Land Use Growth • Enhanced Transit Network • Transportation Demand Management

9. Modeled GHG Reduction 2030 Scenarios • Existing Regional Policy (Baseline) • Low Carbon Land Use • Enhanced Transit • Low Carbon Land Use with Enhanced Transit • Individually test the 4 separate TDM components • Low Carbon Land Use, Enhanced Transit, and all TDM components combined.

10. Low Carbon Land Use Growth

11. San Diego Region Transportation Emission Targets • Through the SB 375 process, ARB will establish mandatory reduction targets for GHG emissions from passenger cars and light-trucks for years 2020 and 2035. • Estimated targets for modeling purposes: • 1990: 12.5 MMT CO2 • 2020 Target: 12.5 MMT CO2 • 2050 Target: 2.5 MMT CO2 • Interpolated 2030 Target: 9.13 MMT CO2 • 2030 Business-as Usual: 19.9 MMT CO2

12. Transportation Demand Management (TDM) Assumptions • Telecommuting Increase Two rates tested: 20% & 40% rate among telecommutable jobs. • Increase Auto Operating Costs (VMT fee) Two rates tested: 1.2¢ per mile & 4.72¢ per mile • 55MPH Freeway Speed Limit • Increase In Parking Costs

13. Modeling Approach • Objective: modify 4-step model to evaluate impact of land use, network, and TDM policies. • Trip generation • Low carbon land use growth • Telecommuting • Trip distribution • Parking pricing: destination zone parking rate • Reduced speed limit • Mode choice • Goal: shift trips from auto modes to transit modes • Mode choice structure • Auto mode utility function

14. Mode Choice Structure

15. Auto Mode Utility Function • where: • m=mode • p=trip purpose • i=income group • t=time period • auu = auto utility by mode, prupose, and income group • auivt = auto in-vehicle travel time between zones by mode and time period • tcoef = time coefficient by type, purpose and income group • termp = terminal time at production end • terma = terminal time at attraction end • pcost = parking cost at attraction end by purpose • toll = toll facility cost by mode and time period • audist = auto distance between zones by mode and time period • ccoef= cost coefficient by purpose and income group • cpm = cost per mile to operate an automobile • nclow = nesting coefficient at lowest level of nest (0.55) • ncmid = nesting coefficient at middle level of nest (0.65) • nctop = nesting coefficient at top level of nest (0.85) Parking cost Auto operating cost In vehicle time Cost term Terminal time

16. Adjustment to Auto Utility Functions • Adjust auto operating costs via VMT fee. • Adjust parking costs at attraction ends Rates were increased in CBD, Urban Centers, Town Centers, and Community Centers. Rural and suburban rates were unchanged.

17. Parking Pricing

18. Annual CO2 Emissions (MMT) Daily VMT (thousands) Initial ResultsIndividual Component Tests Trip Generation Network Costs Post Processes

19. Initial Results

20. Summary • Energy and climate change are on top of federal, state and local government’s political and economic agenda • Transportation sector is the largest source of GHG emissions. • Travel forecasting models play important roles in forecasting GHG emissions. • TDMs, smart growth land use, and enhanced transit network works together to reduce GHG emissions • Use disaggregate models, such as ABMs, for similar analysis

21. San Diego Facts • Jurisdiction Facts • 2,727,030 acres • 4,261.0 square miles • Population • 2008: 3,146,274 • 2030: 3,984,753 • Median Household Income • 2007: $68,388 (current dollars) • Housing Characteristics • Total Housing Units: 1,140,349 • Occupied Housing Units: 1,089,451 • 2030 Regional Growth Forecast • Total Population: 3,984,753 • Housing Units: 1,383,803 • Civilian Employment: 1,828,612