Oct-Nov 2005 1

Measuring the Impact of Urban Sprawl on Vehicle Usage and Fuel Consumption

Tom GolobUniversity of California Irvine

tgolob@uci.edu

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