Incorporating Greenhouse Gas Considerations in Incorporating Greenhouse Gas Considerations in RTP ModelingRTP Modeling

Jerry Walters, Fehr & PeersJerry Walters, Fehr & Peers

CTC Work Group Meeting on CTC Work Group Meeting on RTP GuidelinesRTP Guidelines June 28, 2007June 28, 2007

Linkages between RTP and GHGLinkages between RTP and GHG

Land Use and Transportation

Policies

Land Use

Transportation Nets

Built Environment

TDM

Vehicle Miles Vehicle Trips

Vehicle Speeds

CO2 Emissions Other GHG

Global Warming

Linkage 1. 4D Relationships between Travel and Built Environment

Land Use and Transportation

Policies

Built Environment

Vehicle Miles Vehicle Trips

CO2 Emissions

Land Use and Transportation

Policies

Land Use Vehicle Miles Vehicle Trips

CO2 Emissions

Linkage 2: Induced Investment, Development, Travel

Land Use and Transportation

Policies

Transportation Nets

TDM

Vehicle Miles Vehicle Trips

Vehicle Speeds

CO2 Emissions

Linkage 3: Mobility Return on Investment

Linkage 1: 4D Relationships between

Travel and Built Environment

Daily Vehicle Miles per Person vs. Residential DensitySource: Baltimore Metropolitan Council, 2001 Travel Survey

0

10

20

30

40

50

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0 2 4 6 8 10 12 14 16 18

Households per Acre

Dail

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Hampstead

Odenton

Owings Mills

Dundalk

Reservoir Hill

Butcher's Hill

Brewer's HillBolton Hill

Canton

Federal Hill

Taneytown

Westminster Greens

Westminster Downtown

Havre de Grace

Variation in VMT compared to Trend Scenario

-20.00%

-18.00%

-16.00%

-14.00%

-12.00%

-10.00%

-8.00%

-6.00%

-4.00%

-2.00%

0.00%

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%

-17.3

3%

median: -6.7%

(trend)

Trip generation is directly related to D’s:

DensityDensity dwellings, jobs per acredwellings, jobs per acre

DiversityDiversity mix of housing, jobs, retailmix of housing, jobs, retail

DesignDesign network connectivity network connectivity

DestinationsDestinations regional accessibility regional accessibility

Distance to TransitDistance to Transit rail proximity rail proximity

Shortens trip lengthsShortens trip lengths

More walking/bikingMore walking/biking

Supports quality transitSupports quality transit

Density (jobs and dwellings per acre)

Links trips, shortens distancesLinks trips, shortens distances

More walking/ bikingMore walking/ biking

Allows shared parkingAllows shared parking

Diversity (mix of housing, jobs, retail)

Design (connectivity, walkability)

Destinations (accessibility to regional activities)

Development at infill or close-in locations reduces Development at infill or close-in locations reduces vehicle trips and milesvehicle trips and miles

Transit shares higher within ¼ mile and ½ mile of station

Distance to Transit

Vehicle Trips

Per Capita

VMT

per Capita

Density 4% to 12% 1% to 17%

Diversity 1% to 11% 1% to 13%

Design 2% to 5% 2% to 13%

Destinations 5% to 29% 20% to 51%

4D Elasticity Ranges

Sources: National Syntheses, Twin Cities, Sacramento, Holtzclaw

Land Use Clustering, Mixing, Traditional Land Use Clustering, Mixing, Traditional Neighborhood Design – All Reduce TravelNeighborhood 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.

Shortcomings of Conventional Travel Models Shortcomings of Conventional Travel Models in Assessing Smart Growthin Assessing Smart Growth

• Primary use is to forecast long-distance auto travel on Primary use is to forecast long-distance auto travel on freeways and major roadsfreeways 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 Short-distance travel, local roads, non-motorized travel modes are not addressed in model validationmodes are not addressed in model validation

Typical Model “Blind Spots”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 ModelNetwork in Model Network in FieldNetwork in Field

Typical Model “Blind Spots”Typical Model “Blind Spots”

• Sidewalk completeness, route directness, block Sidewalk completeness, route directness, block size generally not considered.size generally not considered.

Typical Model “Blind Spots”Typical Model “Blind Spots”

• Little consideration is given to spatial relationship Little consideration is given to spatial relationship between land uses within a given TAZ (density)between land uses within a given TAZ (density)

• Interactions between different non-residential land Interactions between different non-residential land uses (e.g. offices and restaurants) not well uses (e.g. offices and restaurants) not well representedrepresented

Potential Sources of SolutionsPotential Sources of Solutions

• Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies (Caltrans)

• Urban Development, VMT and CO2 Emissions, (Smart Growth America)

• Smart Growth INDEX (EPA)

• Travel Characteristics of TOD in California (Caltrans/ Lund, Cervero, Willson)

Caltrans Study ConclusionsCaltrans Study Conclusions

Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies

High-Sensitivity Models

Moderate-Sensitivity Models

Low-Sensitivity Models

Steps to Improve UTMS Sensitivity to Smart-Growth Strategies

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Caltrans Study RecommendationCaltrans 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.

2 Induced Investment Development, Travel

Average VMT Elasticities to Added CapacityAverage VMT Elasticities to Added Capacity

Facility-Specific Studies

Areawide Studies

Short-Term 0 0.4

Medium-Term 0.27 NA

Long-Term 0.63 0.73

Integrated Land Use/ Transportation ModelsIntegrated Land Use/ Transportation Models

• PECASPECAS Users Users: Sacramento SACOG, Caltrans, SANDAG (considering), Ohio DOT, : Sacramento SACOG, Caltrans, SANDAG (considering), Ohio DOT, Baltimore MPO Baltimore MPO

• URBANSIMURBANSIM Users Users: Salt Lake, Seattle, Houston, Honolulu, Detroit: Salt Lake, Seattle, Houston, Honolulu, Detroit

• UPLANUPLAN Users Users: Merced, Wilmington: Merced, Wilmington

• What-IfWhat-If Users Users: Fresno: Fresno

Cautionary Notes on Cautionary Notes on PECAS, URBANSIMPECAS, 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

3 Mobility ROI

Investment in System ContinuityInvestment in System Continuity

Q&AQ&A