Measuring the Impact of Urban Sprawl
on Vehicle Usage and Fuel Consumption
Tom Golob
University of California Irvine
tgolob@uci.edu
ITLS - Sydney Seminar
November 2005
Oct-Nov 2005
1
Objective
• Accurately estimate the impacts of land use density
on car usage
• Important for evaluation of policies concerning
sustainable growth
greenhouse gas emissions
• Evidence in the debate about “car dependency”
Oct-Nov 2005
2
Measuring car usage
• Total distance driven by all household vehicles
result of many travel demand choices:
car ownership
trip generation
mode choice
including drive vs. car passenger
destination choice
• Total fuel usage on all vehicles
vehicle type choice
implicit choice of fleet fuel efficiency
vehicle allocation in multi-vehicle households
Oct-Nov 2005
3
Measuring land use density
• Census data (U.S., 2000, with updates)
Typical variables
housing 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
4
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
5
Previous studies: disaggregate
• Household observations
• Must control for self-selection with respect to
residential location
density related to neighborhood attributes
housing quality
transport level of service by mode
transport preferences
schools, recreation sites, …
cultural and ethnic identity
Oct-Nov 2005
6
Our approach to the problem
• Make choice of residential density endogenous
• Simultaneous equations with three endogenous
variables
residential density
annual mileage
fuel consumption
• All endogenous variables explained by household
characteristics
• The residential density variable affects the two travel
variables
Oct-Nov 2005
7
Simultaneous system: 3 endogenous variables
Exogenous effects
Land use density
income
household structure
Total annual
mileage
ages
number of workers
number of drivers
Total annual fuel
consumption
race and ethnicity
etc.
Oct-Nov 2005
8
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
9
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
10
2001 U.S. NHTS data
• National sample
about 26,000 households
82% have complete data on fuel usage
N = 21,347
• Residential density in terms of
housing units per sq. mi. at census block level
six categories
scaled in terms of category means
Oct-Nov 2005
11
Mileage, fuel usage by residential density
1,400
35,000
total annual fuel
consumption
1,200
30,000
25,000
total annual mileage
800
20,000
600
15,000
400
10,000
200
5,000
Percent of sample:
15%
15%
miles per year
gallons per year
1,000
23%
31%
8%
7%
0
0
<50
50-250
250-1k
1-3k
3-5k
>5k
housing units per square mile in census block group
Oct-Nov 2005
12
Vehicle ownership by residential density
2.2
90%
average vehicle
ownership
2.0
80%
1.8
1.6
60%
1.4
50%
1.2
1.0
% of vehicle-owning households
with a pickup, van, or SUV
0.8
40%
% households
vehicles per household
70%
30%
0.6
20%
0.4
0.2
10%
0.0
0%
<50
50-250
250-1k
1-3k
3-5k
>5k
housing units per square mile in census block group
Oct-Nov 2005
13
Demographics by residential density
3.0
70,000
household income
60,000
2.5
50,000
40,000
drivers per household
1.5
$ US
number
2.0
30,000
workers per household
1.0
20,000
0.5
10,000
0.0
0
<50
50-250
250-1k
1-3k
3-5k
>5k
housing units per square mile in census block group
Oct-Nov 2005
14
The missing data problem
12,000
10,000
8,000
Full energy data
6,000
Missing energy data
4,000
2,000
0
0
1
2
3
4
5
6+
vehicle ownership
Oct-Nov 2005
15
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
16
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
17
Correcting estimates
• Weighting
Weighted Exogenous Sample Maximum Likelihood
Estimator (WESMLE)
Problem:
incorrect coefficient (co)variances
standard errors will be under-estimated
Oct-Nov 2005
18
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
19
Model fit on U.S. national data
• Model structure
19 exogenous variables
recursive structure for the 3 endogenous variables
48 free parameters
• Weighting is important
estimates different from unweighted estimates
bootstrap tests reject alternative specifications
• Model fits well
All overall goodness-of-fit statistics excellent
Oct-Nov 2005
20
National results
Increase in density of 1,000 households / sq. mi.
Change in annual fuel consumption (gals./yr.)
Change in
annual total
mileage on all
Due to
Due to fleet
household
Total
mileage
fuel
economy
vehicles
- 1,630
Oct-Nov 2005
- 74
- 16
- 90
21
Interpretation
• Comparing two households identical in terms of:
income, retirement status
numbers of drivers, workers, children
education of head
race 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 annually
than
• Household B, living in density of 1-3,000 hh./sq. mi.
Oct-Nov 2005
22
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
23
Some exogenous effects
Variable
Number of drivers
Number of workers
Income
Number of children
Education of head
Single-person
Retired household
Asian household
Hispanic household
Black household
Direct effects
Density Distance
Fuel
- (n.l.)
+ (n.l.)
+
+ (n.l)
+ (n.l)
+
+
+
+
(-)
(-)
+
+
(+)
+
Total
fuel
++
- ++
+++
++
--
(n.l. = non-linear)
Oct-Nov 2005
24
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
25
Applications to individual areas
• Need approximately 225 observations
rules-of-thumb based on
number of variables
number 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
26
Contrasting results for 3 NHTS samples
• National
N = 21,347
• Oregon (including 2 counties in Washington State)
N = 325
• California
N = 2,079
Oct-Nov 2005
27
Residential densities for 3 NHTS samples
45%
40%
USA
CA
OR
35%
30%
25%
20%
15%
10%
5%
0%
0-50
50-250
250-1k
1-3k
3-5k
5k+
Housing units per square mile
Oct-Nov 2005
28
Densities for 3 other NHTS samples
45%
40%
Chicago
35%
Los Angeles
30%
New York
25%
20%
15%
10%
5%
0%
0-50
50-250
250-1k
1-3k
3-5k
5k+
Housing units per square mile
Oct-Nov 2005
29
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
30
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
31
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
32
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
Oct-Nov 2005
33