climate change and health impacts of transportation network design
DESCRIPTION
Dr. Lawrence Frank presents on the direct relationship between connectivity of transportation networks, street design and their health implications.TRANSCRIPT
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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
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Transportation and Land use Transportation and Land use Decisions, Land Use Patterns, Decisions, Land Use Patterns, Travel Choices and OutcomesTravel Choices and Outcomes
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The built The built environment environment affects our affects our
healthhealth
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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
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.ProximityProximity
Connect-Connect-ivityivity
2 KM2 KM
1 KM1 KM
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Vancouver Walkability Surface
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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
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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
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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
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Transit Supportive / Walkability Map
•Mixed Use•Density
•Street Connectivity•Amount of Retail
Census Block GroupsCensus Block GroupsLawrence Frank, UBC
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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.
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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
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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
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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.
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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.
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*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
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Analyzing the Fused Grid
Fused Grid Residential QuadrantResulting
Pedestrian Network
Resulting Vehicular Network
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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
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Typology of Connectivity Patterns
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Variable DevelopmentConnectivity and continuity were measured
on distinct modal networks – walking and driving
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Residential Street Patterns in Study Area
Seattle (Queen Anne) Bellevue (East) Redmond (South)
King County, WA – Gridirons to Loop & Culs-de-sac
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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
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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.
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Modeling Physical Activity Outcomes For Contrasting Land
Use Scenarios
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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
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The White
Center / SW 98th
St. Case Study
White Center Neighborhood
SW 98th St. Corridor
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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
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White Center Business District (16th Ave S.)
High concentration of new immigrants and businesses oriented towards themLow-income, good transit service & ridership
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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
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Existing pedestrian connection to 98th St.Steep, overgrown, muddy Little-usedNot ADA accessible
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Greenbridge Hope VI
Redevelop-ment in White Center
Eastern half of 98th St. case study
area
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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
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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.
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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
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LFC, Inc. May. 9, 2009
The Essential Role of Demand
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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.
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High WalkabilityLow Walkability
Prefers a Walkable Community Design
Prefers Auto - Based Community Design
1 2
43N
eigh
borh
ood
Pre
fere
nces
Built Environment
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Stated Preference (Q8a, Q8b, Q8c)Stated Preference (Q8a, Q8b, Q8c)
Street Design and Travel Options
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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”
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-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
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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
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Built EnvironmentTransportation Investments and Land Use
Human BehaviorTravel Patterns and Physical Activity
Environmental QualityAir Quality and Greenspace
Quality of Life
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WorkingAcross Sectors
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The End