Climate Change and Health Climate Change and Health Impacts of Transportation Impacts of Transportation

Network DesignNetwork Design

Dr. Lawrence FrankDr. Lawrence FrankBombardier Chair in Sustainable TransportationBombardier Chair in Sustainable Transportation

University of British ColumbiaUniversity of British Columbia

Transportation and Land use Transportation and Land use Decisions, Land Use Patterns, Decisions, Land Use Patterns, Travel Choices and OutcomesTravel Choices and Outcomes

The built The built environment environment affects our affects our

healthhealth

Philosophical ApproachPhilosophical Approach• Bridging knowledge and actionBridging knowledge and action

– Applied Policy ResearchApplied Policy Research

• Working across disciplinesWorking across disciplines– Connecting Health, Environmental, and Connecting Health, Environmental, and

Transportation Sectors Transportation Sectors

• Building evidence base on the impacts of Building evidence base on the impacts of community design on health and community design on health and environmental outcomesenvironmental outcomes– Quantifying the externalitiesQuantifying the externalities

• Finding strategic opportunities to interveneFinding strategic opportunities to intervene– Evaluating natural experimentsEvaluating natural experiments

.ProximityProximity

Connect-Connect-ivityivity

2 KM2 KM

1 KM1 KM

Vancouver Walkability Surface

Adult Findings - Transit UseBuilt environment characteristics explaining transit use in adults

Any transit trip

Work/school transit trip

Non-work/school transit trip

Higher residential density ++ + +Higher street connectivity ++ + ++Higher commercial density +++ NS +Higher mix of land uses ++ ++ NS

More nearby parks and open spaces NS NS NS

Higher overall neighbourhood walkability ++ ++ ++NS = not significant, '+' = 95% significant; '++' = 99% significant, '+++' = 99.9% significant

Devlin and Frank, 2009

Adult Findings - WalkingBuilt environment characteristics explaining walking in adults

Any walk trip

Work/school walk trip

Non-work/school walk trip

Higher residential density +++ +++ +++Higher street connectivity +++ +++ +++Higher commercial density +++ +++ +++Higher mix of land uses ++ + ++More nearby parks and open spaces +++ + +++Higher overall neighbourhood walkability +++ ++ +++NS = not significant, '+' = 95% significant; '++' = 99% significant, '+++' = 99.9% significant

Devlin and Frank, 2009

Adult Findings - Vehicle UseBuilt environment characteristics explaining vehicle use in adults

Any vehicle trip

Work/school vehicle trip

Non-work/school vehicle trip

Lower residential density +++ ++ +++Lower street connectivity +++ +++ +++Lower commercial density +++ +++ +++Lower mix of land uses +++ +++ +Fewer nearby parks and open spaces +++ NS +++Lower overall neighbourhood walkability +++ +++ +++NS = not significant, '+' = 95% significant; '++' = 99% significant, '+++' = 99.9% significant

Devlin and Frank, 2009

Transit Supportive / Walkability Map

•Mixed Use•Density

•Street Connectivity•Amount of Retail

Census Block GroupsCensus Block GroupsLawrence Frank, UBC

LFC, Inc. May 19, 2009

14

14.5

15

15.5

16

16.5

17

17.5

18

0-36 36-51 51-69 69-159

Household Buffer Quartiles: Intersection / sq. km

VO

C (

gra

ms)

* Controlled for gender, income, age, total number of vehicles in the house

* VOC differences across quartiles significant (p<0.001

Volatile Organic Compounds & Intersection Density (n=2467)

Air Pollution & Neighborhood Design

Source: Frank, L.D. Sallis, J.F., Conway, T., Chapman, J., Saelens, B. Bachman, W. (2006). Multiple Pathways from Land Use to Health: Walkability Associations With Active Transportation, Body Mass Index, and Air Quality. Journal of the American Planning Association.

LFC, Inc. May. 19, 2009

CO2 & Neighbourhood DesignCO2 & Neighbourhood Design

Source: LUTAQH final report, King County ORTP, 2005

8

9

10

11

12

13

0 - 0.1 0.1 - 0.2 0.2 - 0.3 0.3 - 0.4 0.4+

Intersections per acre

CO

2 (K

G)

-- m

ean

dai

ly p

er

per

son

Puget Sound Mode Choice Study 2007

for non-work (home-based other) tours…for non-work (home-based other) tours… Increasing home and destination Increasing home and destination

intersection densities by 10% were intersection densities by 10% were associated with a 2.4% and 2.3% respective associated with a 2.4% and 2.3% respective increase in transit demand.increase in transit demand.

Increasing street network connectivity at Increasing street network connectivity at the home location by 10% was associated the home location by 10% was associated with a 2.8% increase in walking, and with a 2.8% increase in walking, and increasing street network connectivity by increasing street network connectivity by 10% at the destination was associated with 10% at the destination was associated with an additional 2.7% increase in walking.an additional 2.7% increase in walking.– Study Published in Study Published in TransportationTransportation

Seattle / King CountyLUTAQH study

• Travel survey household average = 68 Travel survey household average = 68 intersections / sq km intersections / sq km

• Out of all urban form variables, the greatest Out of all urban form variables, the greatest differences in VMT were observed across differences in VMT were observed across levels of intersection density. levels of intersection density.

• Residents in the most interconnected areas of Residents in the most interconnected areas of the county travel the county travel 26 percent fewer vehicle 26 percent fewer vehicle miles per daymiles per day than those in the most than those in the most disconnected areas.disconnected areas.

• Each quartile of increase in intersection Each quartile of increase in intersection density corresponded with a 14 percent density corresponded with a 14 percent increase in the odds of walking for non-work increase in the odds of walking for non-work travel.travel.

Youth Travel to SchoolStudy (EPA)

Increasing intersection density along the route to school from the median value to the 60th percentile…

• Increases the probability of walking to school by 6.67%, for ages 5-10

• Decreases the average trip distance by 3.23%

• Decreases carbon dioxide by 2.34%• Decreases hydrocarbons by 2.25%• Decreases oxides of nitrogen by 2.65%per student, per trip.

*p<.05, **p<.01, ***p<.001

YOUTH Age Range 5-8 yearsOR (95% CI)

9-11 yearsOR (95% CI)

12-15 yearsOR (95% CI)

16-20 yearsOR (95% CI)

N=847 N=632 N=867 N=815

Intersection highest tertile (vs lowest)

1.7 (1.0-2.9)

1.3 (0.8-2.3)

1.7 (1.1-2.8)*

2.0 (1.1-3.6)*

Density highest tertile (vs lowest)

1.8 (1.0-3.1)

2.3 (1.2-4.3)**

3.7 (2.2-6.4)***

2.0 (1.0-4.1)

Mixed land use (vs no mix) 1.5 (0.9-2.4)

1.5 (0.9-2.5)

2.5 (1.6-3.8)***

1.9(1.0-3.2)*

At least 1 commercial land use (vs 0)

1.5 (0.9-2.4)

1.6 (1.0-2.5)

2.6 (1.7-4.0)***

1.7 (1.0-3.1)

At least 1 recreation/open space land use (vs 0)

2.1 (1.3-3.4)***

1.8 (1.1-2.9)*

2.5 (1.7-3.6)***

1.8 (1.1-2.9)**

controlling for socio-demographics and stratified by age group (Averaged over a two day period)

LOGISTIC REGRESSION ANALYSES PREDICTING LOGISTIC REGRESSION ANALYSES PREDICTING THE ODDS OF WALKING AT LEAST ONCE OVER 2-DAYSTHE ODDS OF WALKING AT LEAST ONCE OVER 2-DAYS

Analyzing the Fused Grid

Fused Grid Residential QuadrantResulting

Pedestrian Network

Resulting Vehicular Network

Non-Motorized V Motorized Connectivity

• Disparities in network connectivity encourage more Disparities in network connectivity encourage more travel in the favored mode, other factors being equal.travel in the favored mode, other factors being equal.

• Tested route directness for Drivers Vs PedestriansTested route directness for Drivers Vs Pedestrians– From home to nearest commercial destinationFrom home to nearest commercial destination

– From home to nearest recreational destinationFrom home to nearest recreational destination

• Assessed relative degree of route directness between Assessed relative degree of route directness between driving and walkingdriving and walking– Relationship with mode choice Relationship with mode choice

• Established four types of communities Established four types of communities – Nexus of high and low levels of pedestrian and vehicle Nexus of high and low levels of pedestrian and vehicle

connectivityconnectivity

Typology of Connectivity Patterns

Variable DevelopmentConnectivity and continuity were measured

on distinct modal networks – walking and driving

Residential Street Patterns in Study Area

Seattle (Queen Anne) Bellevue (East) Redmond (South)

King County, WA – Gridirons to Loop & Culs-de-sac

Findings - DescriptiveWalking Mode Share and Street Connectivity

Disparate Street Connectivity and associated

Walk Shares

(by person to commercial)

Pedestrian Connectivity

Low High

Vehicular

connectivity

Low

SE and Central Bellevue; SW Seattle – Loop and Culs-de-Sac

Mean Mode Share:10% walking

n = 985

Queen Anne, Capital Hill (Seattle) – Modified grid with connectors

Mean Mode Share:

18% walkingn = 66

High

N and , – Grid and major streets w/o sidewalks

Mean Mode Share:10% walking

n = 59

Downtown and Older Neighbourhoods – Gridiron

Mean Mode Share:14% walking

n = 966

Interpretation

• A modification of only 10% on the relative connectivity of neighborhood streets (gridiron to Fused Grid) is associated with a 25.9% better odds of meeting, through walking, public health recommendations for daily physical activity.

• Increasing relative network density by the same 10% => +18.2% odds of active levels of walking.

Modeling Physical Activity Outcomes For Contrasting Land

Use Scenarios

I-PLACES Impact Assessment Model: the Concept

Use research results on the relationships between

Integrate these findings into an existing model structure

Urban Form Patterns Residential DensityLand Use MixStreet Network ConnectivityRetail Floor Area Ratio

OutcomesTransit UseVehicle TravelWalkingPhysical activityObesity / Body Mass IndexCO2 & pollutants from

and

The White

Center / SW 98th

St. Case Study

White Center Neighborhood

SW 98th St. Corridor

Adding new modules to the I-PLACE3S model for King County

Climate change and air quality (outcomes: CO2, NOX, HC, and CO; vehicle trips and VMT)

Public health (outcomes: physical activity, BMI, walk and bike trips)

Tools for Scenario Planning

White Center Business District (16th Ave S.)

High concentration of new immigrants and businesses oriented towards themLow-income, good transit service & ridership

Buildout Scenario

Full buildout of Greenbridge public housing Pedestrian connection links Greenbridge & 98th St.

CorridorFull buildout at maximum density

High density mixed use development (pink)Mid-rise residential development (dark orange)

Approx. 2500 households, 1800 employees

x

Existing pedestrian connection to 98th St.Steep, overgrown, muddy Little-usedNot ADA accessible

Greenbridge Hope VI

Redevelop-ment in White Center

Eastern half of 98th St. case study

area

Scenario Results

CO2 (kg) / DU

NOX (grams) /

DU

HC (grams) /

DU

CO (grams) /

DU

Car Vehicle Miles /

DU

Transit Person Miles /

DU

Walk / Bike

Miles / DU

BMI / Adult

Minutes of

Physical Activity /

Adult

BASE CASE 14.17 47.62 51.69 580.00 48.82 12.67 3.13 24.74 37.06

Buildout with ped connection

13.94 46.70 50.61 569.82 47.85 12.99 2.73 24.10 41.94

Base Case + Transit

13.13 44.62 48.62 542.38 45.64 13.34 3.13 24.60 37.06

Buildout + Ped Connection + Transit

12.90 43.70 47.54 532.20 44.67 13.65 2.73 23.96 41.94

Seattle - King County Development Review

• Application of analysis on GHGs from transportation

• Help achieve goals for GHG reduction (80 percent below 2007 levels by 2050)

• Can condition or deny proposals that have significant, adverse impacts on the environment due to their GHG emissions.

• If passed by the Council, King County will be the first local government in the nation to add GHG emissions to environmental review of construction projects.

LFC, Inc. May. 19, 2009

Final Map of CO2

emissions from

transportation

Includes: Local urban form

(land use mix, intersection

density, retail FAR)

Regional location (auto travel time

Transit accessibility & travel time

Demographics

LFC, Inc. May. 9, 2009

The Essential Role of Demand

Results – Urban Form + Major Progress

• All else equal, households living in the most walkable King County neighborhoods were 54 percent more likely to meet the 8.4 daily mile threshold.

• Each ten-minute decrease in regional Each ten-minute decrease in regional transit travel time increased the odds transit travel time increased the odds of meeting the VMT target by 11 of meeting the VMT target by 11 percent. percent.

High WalkabilityLow Walkability

Prefers a Walkable Community Design

Prefers Auto - Based Community Design

1 2

43N

eigh

borh

ood

Pre

fere

nces

Built Environment

Stated Preference (Q8a, Q8b, Q8c)Stated Preference (Q8a, Q8b, Q8c)

Street Design and Travel Options

There is a latent demand for more connected streets –

Even in Atlanta. A

Cul-de-sacs, must drive for all trips

B Can walk,

cycle, transit; connected

streets

Your current neighborhood is more like:

53% (765) More like “A”

34% (497) More like “B”

Neighborhood you'd hope to find:

29% (423) Would like “A”

41% (591) Would like “B”

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-6 -4 -2 0 2 4 6

Neighborhood Walkability (Objective)

Pre

fere

nc

e (

Su

bje

cti

ve

) Quadrant 1: UnmatchedWalkability -- Low Preference -- Walk

Quadrant 4: UnmatchedWalkability -- HighPreference -- Auto

Quadrant 3: Matched Walkability -- Low

Preference -- Auto

Quadrant 2: MatchedWalkability -- High Preference -- Walk

Low High

Au

toW

alk

PREFERENCE VS PREFERENCE VS NEIGHBORHOOD DESIGNNEIGHBORHOOD DESIGN

Preference for Neighborhood

Type

Walkability of Current

Neighborhood16.0% 36.6 14.9%(188) (188) (161)

33.9% 25.8 11.7%(446) (446) (386)

3.3% 43.0 21.4%(246) (246) (215)

7.0% 25.7 21.6%(43) (43) (37)

FIGURE 12 - Walking, Driving and Obesity by Neighborhood Preferenceand Walkability

Percent Taking a Walk Trip

(n)

Average Daily Vehicle Miles

Traveled (n)

Percent Obese

(n)

IV Low High

Walkability & Preference Groups

Low Low

II

III

I High Low

High High

Built EnvironmentTransportation Investments and Land Use

Human BehaviorTravel Patterns and Physical Activity

Environmental QualityAir Quality and Greenspace

Quality of Life

WorkingAcross Sectors

The End