Advanced Modeling System for Forecasting Regional Development,

Travel Behavior, and the Spatial Pattern of Emissions

Brian J. Morton

Elizabeth Shay

Eun Joo Cho

July 13, 2005

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UNC-CH/NCSU Project Team

• Land use and travel behavior modeling– UNC-CH– NCSU

• Emissions estimation– NCSU

• Air quality modeling– UNC-CH

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EPA STAR Grant

• Regional Development, Population Trend, and Technology Change Impacts on Future Air Pollution Emissions

• EPA’s interests– Ground level ozone and fine particulate matter– How locations of emissions might change in

response to future land development patterns– Current tools used to estimate emissions do not

capture long-term changes in regional development patterns

– Long time frame: 50 years

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Key Research Questions• How might regional development patterns,

over 50 years, influence quantity and spatial pattern of emissions from transportation in Charlotte?

• Could realistic development patterns reduce transportation emissions by 10-20% or more?

• How would different development patterns affect quality of life?– Ozone and of fine particulate matter– Human exposure– Other indicators

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Reasons for Selecting Charlotte

• Growing metro area in North Carolina

• Data-rich site• Recent travel survey• Designated 8-hour ozone

nonattainment area• ReVA• SEQL• Future transit metropolis?

Charlotte transportation system in 1940

Charlotte in 2050?

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Overview of Research Design• Development drivers

– Market and non-market incentives and constraints on development characteristics and location

• Land use model– Markets for land and for floor space

• Travel behavior model– Motorized and nonmotorized modes

• Vehicle emissions model– Engine load approach (same as EPA’s MOVES)

Integrated models—

TRANUS

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TRANUS

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Activities-Land Use System

Productive sectorsHousehold sectorsFloorspaceLand Commodity flows

Production costs

Traveler flows

Equilibrium pricesof land and floorspace

Consumption ofland and floorspace

Source: Modelistica, 2004

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Transportation Model

Trip generation

Trip distribution

Mode split

Trips assignment

Elastic trip generation

Elastic mode split

Probabilistic assignment

Urban form

-Mix of uses

-Density

-Infrastructure

-Parking pricing

-Regional access

Vehicle ownership model

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TAZ Transect for Describing Neighborhoods

• TAZ Transect classifies neighborhoods based on:– Land use characteristics (density and use)– Transportation (street design and modes)

• TAZ Transect provides a palette of neighborhoods for scenario assessment

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Two Types of Neighborhood

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Emissions Estimation

EmissionRate

Estimation

Micro-scale

Emissions

Meso-scale

Emissions

Macro-scale

Emissions

VehicleActivity

Estimation

EmissionsEstimation

Speed

Acceleration

Road Grade

Load

Ambient Cond.

Factors AffectingEmission Rates

SelectContext-SpecificDefaults

Technology.

Etc.

Average Speed

Data FromTravel Forecasts

Trans. Mode

Etc.

VMT

DrivingModes

. . .

EnergyUse

Facility-SpecificDriving Cycles(Real-World,

On-Board Data)

ActivityKnowledge

Base

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Scenario Development: Two Approaches

• Paint the landscape with new land uses and/or changes to the transportation system

• Change market and non-market incentives and constraints on development, with or without transportation system changes

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Summary of Modeling Approach

Classify zones according to

transect

TAZ Transect

Identify future scenarios based on

typology

Translate scenarios into land market

(change constraints, impose new tastes,

etc.) & transportation system

Multimodal travel forecasts sensitive to typology & exogenous

factors

Estimate emissions

from on-road mobile

sources

Run air quality model

Study area: Charlotte

Exogenous Factors:

Population agingIPCC’s parametersVehicle fleet mixVehicle technology

Run selected land use/transport

forecasting model

Future locations of employment centers &

residences

Estimate exposures

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Expected Results and Benefits

• State-of-the-art simulation model for investigating effects of development on spatial pattern and quantity of emissions from mobile sources

• Scenario assessments for Charlotte– What proportion of area would have to be developed in

a compact manner to reduce emissions by 10% or 15%?

– Is a 20% emission reduction feasible with any reasonable forecast of market penetration of smart growth?

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Analytical Innovations

• TAZ Transect– Quantitative typology of land-use patterns at

the neighborhood level (transportation analysis zone)

– Tool for describing development scenarios

• Land use model– Based on economic theory of how

development occurs– TRANUS (applied in Europe and South

America)

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Additional Analytical Innovations

• Travel behavior model– Travel options include bicycle and walking– Trip generation, destination choice, and

modal choice are sensitive to attributes of built environment such as pedestrian friendliness

• Vehicle emissions model– Detailed emissions profile for transit vehicles– Next generation emission factor model

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Timeline of Major Analytical Tasks

• 2005– Obtain data– Construct land use and transportation models

• 2006– Construct baseline TAZ Transect– Refine land use and transportation models– Develop and analyze development scenarios

• 2007– Additional scenario analysis – Forecast air quality with Models3/Community

Multiscale Air Quality modeling system

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Working Together…

• Already working together on data – thank you, thank you, thank you!

• Additional ways of working together– Land use modeling– Transportation modeling– Developing scenarios– Evaluating scenarios– Others?

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Contact Person and Web Site

• Brian J. Morton, Ph.D., Project Managerbjmorton@unc.edu

(919) 962-8847

Center for Urban and Regional Studies

University of North Carolina at Chapel Hill

• http://epastar.unc.edu/index.htm