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Measuring the Impact of Urban Sprawl on Vehicle Usage and Fuel Consumption. Tom Golob University of California Irvine [email protected] ITLS - Sydney Seminar November 2005. Objective. Accurately estimate the impacts of land use density on car usage - PowerPoint PPT Presentation
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Oct-Nov 2005 1
Measuring the Impact of Urban Sprawl on Vehicle Usage and Fuel Consumption
Tom GolobUniversity of California Irvine
ITLS - Sydney SeminarNovember 2005
Oct-Nov 2005 2
Objective
• Accurately estimate the impacts of land use density on car usage
• Important for evaluation of policies concerningsustainable growth
greenhouse gas emissions
• Evidence in the debate about “car dependency”
Oct-Nov 2005 3
Measuring car usage
• Total distance driven by all household vehiclesresult of many travel demand choices:
car ownershiptrip generationmode choice
including drive vs. car passenger
destination choice
• Total fuel usage on all vehiclesvehicle type choice
implicit choice of fleet fuel efficiency
vehicle allocation in multi-vehicle households
Oct-Nov 2005 4
Measuring land use density
• Census data (U.S., 2000, with updates)
Typical variableshousing units per sq. mi. (per
area unit) population per sq. mi.jobs per sq. mi.
Resolution (U.S.)Census tract (average size
4,000 persons)Census block groups (average
~1,000)
Other GIS functionality available
Oct-Nov 2005 5
Previous studies: aggregate
• Compare averages for cities, zones, neighborhoods
• Impossible to control effectively for differences in:
Household characteristics
Transport infrastructure
Transport levels of service
Arrangement of land uses
Culture
Oct-Nov 2005 6
Previous studies: disaggregate
• Household observations
• Must control for self-selection with respect to residential location
density related to neighborhood attributeshousing qualitytransport level of service by
mode transport
preferencesschools, recreation sites, …cultural and ethnic identity
Oct-Nov 2005 7
Our approach to the problem
• Make choice of residential density endogenous
• Simultaneous equations with three endogenous variables
residential densityannual mileagefuel consumption
• All endogenous variables explained by household characteristics
• The residential density variable affects the two travel variables
Oct-Nov 2005 8
Simultaneous system: 3 endogenous variables
Oct-Nov 2005 9
Data requirements
• Annual mileage for all household vehicles
derived from odometer readings or imputed
• Fuel usage calculations for all vehicles
according to vehicle make, model and vintage
• Census data on land use density
matched to household location
Oct-Nov 2005 10
Data availability
• The 2001 U.S. National Household Transportation Survey (NHTS) data
annual mileage for all household vehicles
fuel usage for all household vehicles
census data on land use density
24-hour travel diaries for all members
28-day record of long-distance travel (50 mi.+)
demographics and socio-economics
Oct-Nov 2005 11
2001 U.S. NHTS data
• National sampleabout 26,000 households82% have complete data on fuel usageN = 21,347
• Residential density in terms ofhousing units per sq. mi. at census block
levelsix categoriesscaled in terms of category
means
Oct-Nov 2005 12
Mileage, fuel usage by residential density
0
200
400
600
800
1,000
1,200
1,400
<50 50-250 250-1k 1-3k 3-5k >5k
housing units per square mile in census block group
ga
llon
s p
er
ye
ar
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
mile
s p
er
ye
ar
total annual fuelconsumption
total annual mileage
15% 23% 31% 8% 7%15%
Percent of sample:
Oct-Nov 2005 13
Vehicle ownership by residential density
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
<50 50-250 250-1k 1-3k 3-5k >5k
housing units per square mile in census block group
ve
hic
les
pe
r h
ou
se
ho
ld
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
% h
ou
se
ho
lds
average vehicleownership
% of vehicle-owning households with a pickup, van, or SUV
Oct-Nov 2005 14
Demographics by residential density
0.0
0.5
1.0
1.5
2.0
2.5
3.0
<50 50-250 250-1k 1-3k 3-5k >5k
housing units per square mile in census block group
nu
mb
er
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
$ U
Sdrivers per household
workers per household
household income
Oct-Nov 2005 15
The missing data problem
0
2,000
4,000
6,000
8,000
10,000
12,000
0 1 2 3 4 5 6+
vehicle ownership
Full energy data
Missing energy data
Oct-Nov 2005 16
Biases due to missing data
• Probability of being missing related to levels of the endogenous variables
• Classical sample selection problem
• Reference:Tom Golob and Dave Brownstone (2005)
The Impact of Residential Density on Vehicle Usage and Energy Consumption
Working paper EPE-011, University of California Energy Institute
on the web at: University of California eScholarship Repository
Oct-Nov 2005 17
Correcting estimates
• Structural approach
Heckman selection modeling
Add equation to construct a new hazard for sample inclusion
Problems:Results are sensitive to
model specification
Inconsistency when variable sets overlap
Oct-Nov 2005 18
Correcting estimates
• Weighting
Weighted Exogenous Sample Maximum Likelihood Estimator (WESMLE)
Problem:incorrect coefficient
(co)variances
standard errors will be under-estimated
Oct-Nov 2005 19
Estimation method
• Weighted estimator (WESMLE)
• Estimates using weighted data are robust
Standard errors seriously downward biased
Standard errors are accurately estimated using Wild Bootstrap method
• Heteroskedasticity consistent covariance matrix estimator
• Cannot reject that errors are exogenous using Structural (Heckman) approach
Oct-Nov 2005 20
Model fit on U.S. national data
• Model structure19 exogenous variablesrecursive structure for the 3 endogenous
variables48 free parameters
• Weighting is importantestimates different from unweighted
estimatesbootstrap tests reject alternative
specifications
• Model fits wellAll overall goodness-of-fit statistics excellent
Oct-Nov 2005 21
National results
Increase in density of 1,000 households / sq. mi.
Change in annual total
mileage on all household
vehicles
Change in annual fuel consumption (gals./yr.)
Due to mileage
Due to fleet fuel economy
Total
- 1,630 - 74 - 16 - 90
Oct-Nov 2005 22
Interpretation
• Comparing two households identical in terms of:income, retirement statusnumbers of drivers, workers, childreneducation of headrace and ethnicity
• Household A, living in density of 3-5,000 hh./sq. mi.
will drive 3,300 fewer miles on all vehicles consuming 180 less gallons of fuel annuallythan
• Household B, living in density of 1-3,000 hh./sq. mi.
Oct-Nov 2005 23
Important exogenous variables
• Income
• Number of drivers
• Number of workers
• Whether household single-person
• Number and age of children
• Education of head(s)
• Whether household retired
• Race/ethnicity
Oct-Nov 2005 24
Some exogenous effects
Direct effects Total
Variable Density Distance Fuel fuel
Number of drivers - (n.l.) + (n.l.) ++
Number of workers + + (n.l) + (n.l) - ++
Income - + + +++
Number of children - + ++
Education of head - -
Single-person + -
Retired household - (-) (-)
Asian household + - --
Hispanic household + (+)
Black household + - (n.l. = non-linear)
Oct-Nov 2005 25
Tests of alternative models
• Error term correlations
all can be rejected (no correlation with sig. t )2 = 6.35; 3 d-o-f (not sig.)
• Feedbacks
drive more = move to higher density(t = 1.27) 2 = 1.52; 1 d-o-f
(not sig.)
higher fuel usage = move to higher density(t = 1.03) 2 = 1.02; 1 d-o-f
(not sig.)
• Base model best according to Bayesian criteria (CAIC)
Oct-Nov 2005 26
Applications to individual areas
• Need approximately 225 observationsrules-of-thumb based on
number of variablesnumber of free parameters
• Translates to 275 at 82% non-missing data
• 2001 U.S. NHTS data will support modeling for:
30 states
17 metropolitan areas
Oct-Nov 2005 27
Contrasting results for 3 NHTS samples
• NationalN = 21,347
• Oregon (including 2 counties in Washington State)N = 325
• CaliforniaN = 2,079
Oct-Nov 2005 28
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0-50 50-250 250-1k 1-3k 3-5k 5k+
Housing units per square mile
USACAOR
Residential densities for 3 NHTS samples
Oct-Nov 2005 29
Densities for 3 other NHTS samples
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0-50 50-250 250-1k 1-3k 3-5k 5k+
Housing units per square mile
Chicago
Los Angeles
New York
Oct-Nov 2005 30
Results by area
Increase in density of 1,000 households / sq. mi.
Change in annual total mileage on all
household vehicles
Change in annual fuel consumption
Due to mileage
Due to fuel economy
Total
U.S. - 1,630 - 74 - 16 - 90
OR - 1,340 - 57 - 20 - 77
CA - 1,000 - 43 - 16 - 59
Oct-Nov 2005 31
Extensions
• Results similar, but less precise when using other available NHTS density variables
• Can be extended to estimate effects of residential density on specific aspects of travel
e.g., trips by public transport
a different estimation method should be used for limited dependent variables (those with large spikes at the value zero)
that estimation method requires larger sample sizes (perhaps 1,500 minimum)
Oct-Nov 2005 32
Conclusions: methodological
• In measuring the effects of residential density, it is important to control for:
selectivity bias in residential location choice
missing data related to the endogenous vars.
• Survey data needs:
odometer readings
vehicle specs. (make, model, vintage of all)
residential location
• Appropriate land use data can easily by added to survey data sets using GIS
Oct-Nov 2005 33
Conclusions: Empirical
• Lower residential density does lead to greater vehicle usage, controlling for other influences
• Greater fuel consumption is due to both longer distances driven and vehicle type choice
• Results show the importance of using disaggregate data and controlling for self selection
many household characteristics
including race and ethnicity