Integrating an ABM with a Health Co-Benefit Analysis Tool

15 Pages·2014·0.91 MB·English

Using Activity Based Travel Demand
Models to Estimate Health
Co-Benefits of Land Use and
Transportation Plans
Nicholas J. Linesch
Institute of Transportation Studies – Davis
[email protected]
TRB Innovations in Travel Demand Modeling
April 30, 2014

Co Authors:
Caroline Rodier, PhD – UC Davis
Richard Lee – Mineta Transportation Institue
Climate Change and Public Health
• Climate change no. 1 public health threat in 21st Century
• California 12th largest greenhouse gas emitter in world
• Transportation is the largest source of GHGs in California –
38% of total (168 MMT CO E in 2011)
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– Personal passenger vehicles account for 30%
(79% of 38%)
• How can we reduce GHG emissions in transportation?
– Increase efficiency of vehicles and fuels
– Reduce vehicle miles traveled (less trips, mode
switching (SOV to mass transport), walking/bicycling
(active transport)

? ? ? ?
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Groundbreaking Health Co-Benefits Research
• 2009 London Study: estimated the health impacts of alternative strategies for
reducing carbon dioxide emissions from transport.
– Lower carbon driving
• Lower carbon emission motor vehicles/fuels
– Increased active travel
• Replacing urban car and motorcycle trips with
walking or bicycling
Dr. James Woodcock
– Shift from 10 to 30 minutes/day of walking and bicycling:
19% Cardiovascular Disease
15% Diabetes
13% Breast Cancer =
 8% Dementia
 38% CO Emissions
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* Woodcock J, Edwards P, Tonne C, Armstrong BG, Ashiru O, Banister D, et al. Public health benefits of
strategies to reduce greenhouse-gas emissions: urban land transport. The Lancet 2009;374:1930-1943.
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The Model Integrates Health and Travel Data
Physical
Activity
Air
Health
Survey Pollution
Vehicle
Travel
Emissions
Survey
Model
Air
Travel
Demand Shed
Model Model
Traffic
ITHIM
Injuries
Traffic U.S.
Collisions Census
Health
Scenarios
Statistics
Scenario
vs. BAU
 Premature Deaths
 Years of Life Lost
 Years Living with Disability
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Road Traffic Injury Risk
• Road Traffic Injuries: a mechanistic model based on injuries per miles
traveled by the victim (PMT) and the striking vehicle (VMT)

Number of Injuries/Fatalities

Striking Vehicle, SV
Victi m, V b p m c d h

Bicycle b r r r r r r
bb bp bm bc bd bb
Pedestrian p r r . . . .
pb pp
Motorcycle m r r r . . .
mb mp mm
Car c r etc . . . .
cb

Bus d r . . . . .
db

Truck h r . . . . .
hb

Injuries
• Baseline Injury Risk: R  Victim0

0
PMT VMT
Victim0 StrikingVeh0
• Scenario Injuries: I  R PMT VMT
S1 0 VictimS1 StrikingVehS1
• Stratified by roadway type and severity (fatal, serious)

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Road Traffic Injuries : Example of How ITHIM Works
• Annual mean per capita distances traveled by mode
Base Scenario Layout
Baseline Scenario

Mode Miles Miles
Walk 100 350

Bike 50 250

Car-total 7,850 7,400
Driver 5,888 5,550

Passenger 1,963 1,850
Total 8,000 8,000

•
“Safety in Numbers”:
Observation that pedestrian
and bicycle injury rates decline
as their trip mode share or
distance traveled increases;
approximately a square root
function

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Air Pollution (PM2.5): Example of How ITHIM Works
1.30
• r
Dose-response of cardio-pulmonary and PM e
2.5 cn 1.25
a
from literature LN(RR) =0.008618*(b - b ), C
1 0 g 1.20
n
where b is ambient PM2.5 u
L
f 1.15
o
• Emissions model (e.g. EMFAC2011) k
siR 1.10
e
 v
Input assumptions re: car fleet, VMT, year it 1.05
a
le
 R 1.00
Output: Tons/day
0 10 20 30
Baseline Scenario

Mean PM Concentration (g/m3)
Other Other 2.5
Cars Vehicles Cars Vehicles

PM2.5, tons/d 3.75 3.5 3.54 3.3
•
Airshed Model (mobile + stationary sources of emissions)

Reduction of PM as a function of % change in VMT in nanograms/m3
2.5

Y = 3.17*%VMT + 0.23 = -17.9525 ng/m3
Baseline Scenario

Expressed as population-weighted means: PM (g/m3) 9.50 9.48
2.5
•
PAF = e(0.008618 *(9.48-9.50)) = 0.9998453

•
Existing burden of lung cancer = DALYs (approx. for SCAG Region)
•
 BD = BD – BD = BD-(BD  PAF) = 78,300 – 0.9998453* 78,300 = 12 DALYs
baseline scenario
gained
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Synthesizing the Results: Example of How ITHIM Works
Source of Co-Benefit/ DALYS per 105
Harm DALYs/y population/y
Physical activity 24,276 622.5
Road Traffic Injuries -10,015 -256.8
PM2.5 3 0.1
Total 14,263 365.7
Annual Health Co-Benefits /
Harms of Alternative Scenario
700
3 PM
n 2.5
o 500
it
a Physical activity
lu
p 623
o 300
P
5
0
1
r 100
e
p
s
Y
L -100 -257
A Road Traffic Injuries
D
-300
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ITHIM Basics: What ITHIM Doesn’t Do ‒
Scenario Development vs Health Co-benefits Calculation
Scenario Development ITHIM
Other Key Inputs
Gray Boxes =
 Burden of Disease/Injury
ITHIM Inputs
 SWITRS Injuries
 Non-Transport Phys. Act.
PM2.5
Travel Demand Air Quality
Model  Emissions model
 Air shed model
 Distance to Transit
 Destination Accessibility
Project Travel Distance
 Diversity of Land Use ITHIM
Policy day/person  mode
 Density (pop. & jobs)
Scenario
 Design (network connectivity)
Change in Burden
of Disease/Injury
Land Use Model
Other Methods to Set Key Input
 Local/regional benchmark
 Administrative target , physical activity
 Short trip substitution
 Backcast to optimize health/GHG

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