Location, Vehicle Miles of Travel,
and the Environment:
A Chicago Case Study
Marshall Lindsey
October 21, 2010
Transportation
Center Seminar
Advised by:
Kimberly Gray
Pablo Durango-Cohen
Joseph Schofer
U.S. Primary Energy Consumption by Source and Sector, 2007
http://www.eia.doe.gov/emeu/aer/pdf/pecss_diagram.pdf
U.S. Transportation
GHG Emissions by
Source, 2006
U.S. Environmental Protection Agency, 2006.
Our Focus:
Location has a significant impact on how
household demographic, employment,
and urban-form variables influence
driving behavior.
Our Path:
• Explore Spatial Patterns
• Run scenario tests: possible
energy savings?
• Evaluate statistical methods: a look
at the puzzle pieces
Our Path:
• Focused policy, solution exploration
Exploring Chicago Spatially
In Chicago, what is the impact of location on VMT,
energy consumption, and greenhouse gas
emissions?
What are possible roads to reduction?
Chicago Metropolitan
Agency for Planning
(CMAP) Data, 2007-2008
- Travel Survey of
~10,500 households
- Travel & demographic
data
Attempts at Regression
Household Variables and Their Model Coefficients
Adjusted
R2 = 0.28
t-Statistic values of ≥ 2 indicate
significance
Average
POV VMT
Range of Average
VMT Cell Values :
18.9 – 52.9
Average POV
Energy
Range of Average
Energy Cell Values:
9.2 – 31.9
Values are
x10 MJ
Effect of
Fleet
1.3
1.1
.9
1.5
1.9
low mpg
range: < 22
.7
.6 1.0
1.21.0
1.1 .7 .9 1.2 1.3 .9 .9 1.2
1.0 1.1
1.1 1.0 1.0
1.0 .8 .9 .9 .5 .5 .4
1.4 1.1 .7 1.0
1.6
.8 1.1 .8 .6 .4
.5
.6 1.3
0.2 – 2.1
1.4 .6 1.1 .7 .8 .8 1.7 .8 .7
1.2 1.0 1.5 .5 .3 1.0
.9
Range of
Ratios :
.7 .8 1.3 1.4 .6 .6
1.3 .8 .6 .2 1.8 1.11.2 .7 .9 .5 .2
low mpg VMT
mid mpg VMT
1.3
.8
.7
Cell Ratio =
10
miles
1.7 .9
mid mpg
range: 23 - 26
5
.9
.7 .8 .7 .6 .5
2.2 .7 .8 1.1 .6
1.4 .8 .7 .7 1.2 2.1
1.3
.9 1.3
1.9
.7
1.2
15
Effect of Fleet
Changing the Profile of Emissions With
Technological Advancement
EU CAFE standard scenario:
Shift from today’s US CAFE standards to 49 mpg
Conclusions For Part 1:
Spatial Analysis
• Spatial distribution of VMT and energy
consumption/CO2 emissions is a
function of distance from the city center.
• Higher energy values are result of
high VMT and low efficiency vehicle
use, especially at fringe.
• Scenario tests show that changes in
CAFE standards and fleet mix give
significant energy/CO2 savings
• Technological advancement should
be the policy focus: benefits realized
in shorter term over land use change
Lindsey, et al. Transportation Research Part D (2010), doi:10.1016/j.trd.2010.08.004.
Getting People to Drive Less (to Work)
How does rail transit accessibility relate to the
propensity for mode shift?
Rapid Rail Transit
(Chicago Transit
Authority– CTA)
Commuter Rail
(Metra)
Factors Affecting Traveler’s Choice to Ride Transit:
- Frequency
- Structure
- Reliability
- Price
- Accessibility of station
Brons, M. et al (2008)
Creating Analysis Groups/Subgroups
0.25 mi
Subgroups
JTW Group
JTW =
Journey-ToWork
0.5 mi
1 mi
Home
(Origin)
Work
(Destination)
Who Are JTW Candidates for Mode Shift?
CTA Example:
1 mi
Subgroup: 1418 Trips
- HH/destination both
w/in 1 mi of station
Group: 1995 Trips
- HH w/in 1 mi of station
Home
(Origin)
Work
(Destination)
= 71%
Who Are JTW Candidates for Mode Shift?
Metra Example:
1 mi
Subgroup: 1957 Trips
- HH/destination both
w/in 1 mi of station
Group: 3482 Trips
- HH w/in 1 mi of station
Home
(Origin)
Work
(Destination)
= 56%
Mode Share of JTW Subgroups
Mode Share for All JTW Trips (2007 – 2008)
Mode Share of Select
CTA Subgroups
1 mi/1 mi
Mode Share of Select
Metra Subgroups
0.25 mi/0.25 mi
0.5 mi/0.5 mi
Home/Work
1 mi/1 mi
0.25 mi/0.25 mi
0.5 mi/0.5 mi
Energy Savings From JTW Mode Shift:
POV Æ Transit
Total JTW POV
Energy:
Accumulated energy
for all JTW trips that
were driven by car
% Reduction in Total
JTW POV Energy
Energy reduced by
subgroup trips
shifted from POV to
transit
0.25 mi
0.5 mi
1 mi
Home
Work
Conclusions for Part 2:
Shifting to Rail Transit
• Even with current infrastructure and
settlement patterns,
behavioral/operational change could
reduce emissions
– Mode shift is feasible based on
accessibility
– Maximum savings from mode shift–
CTA: 7%, Metra: 20%, Combined: 24%
• Accessibility not being a deterrent is
promising, however, several other
factors could impede mode shift:
network limitations.
Transportation Research Part A (2010), doi:10.1016/j.tra.2010.07.003.
Why Is There Variation Among Cells?
What impact does heterogeneity have on
our understanding of household VMT?
What policy implications can we extract
from this understanding?
Heterogeneity: observed and unobserved
– Variation in household tastes
– Variation in local land uses
Attempts at Regression
Household Variables and Their Model Coefficients
Adjusted
R2 = 0.28
t-Statistic values of ≥ 2 indicate
significance
Utilizing Mark Segmentation Techniques
Heterogeneous
Population
Homogeneous
Segments
Cumulative Distribution
Segmentation via Finite Mixture Modeling
Cumulative distribution
function used to verify
model fitting based on
various segmenting
Model Definition
Cumulative Distribution
Cumulative distribution functions: a closer look
Goodness of Fit Test
indicates diminishing
returns in model
correspondence > 4
modes
Model Used to Cluster
Household into Bins
4 Modes = 4 Bins
Average Daily VMT/bin
Effects of Heterogeneity
Expected Cumulative Distribution Function:
Cumulative Distribution
Profile when unobserved heterogeneity
has less of an effect
1
Lo
Hi
0
Distance from CBD
Effects of Heterogeneity
Cumulative distribution functions: Testing Variables
Effects of Heterogeneity
More Variable Testing
Number of
Licensed
Drivers
Lo
Mid 1
Mid 2
Hi
1
0.55
0.31
0.11
0.03
3078
2
0.26
0.34
0.25
0.15
4780
3
0.14
0.26
0.33
0.27
717
4
0.07
0.19
0.39
0.35
161
Mixture Proportions
Number of
Households
Household
Income
Driving Behavior of Households
in Segments Examined Regionally
≥≥30
30mi
mi
≥≥25
25mi
mi
<<15
15mi
mi
<<10
10mi
mi
<<55mi
mi
Effects of Heterogeneity
Comparing North Side of Region to South Side
% Differences Between North and South
- Shaded cells are where North Side values are greater
Average Daily VMT/bin
Effects of Heterogeneity
Comparing North Side of Region to South Side
% Differences Between North and South
- Shaded cells are where South Side values are greater
Average Daily VMT/bin
Conclusions for Part 3:
Heterogeneity
• Finite mixture modeling allows us to
predict with confidence the profile of
drivers throughout an urban space, Lo to
Hi
• Market segmentation enables us to identify
which variables provide for heterogeneity
and in what manners
• Heterogeneity in VMT data due in part to
profile of variables on the Northern or
Southern region of city
• Medium to High (Mid1, 2, and Hi groups) driving
impacted
• Policy development could benefit from consideration
of these results
Targeting the Suburbs of Chicago
How can our greater understanding of the profile
of suburbs lead to strategies for diminishing
emissions and energy consumption?
Canonical
Correspondence
Analysis (CCA)
• Ordination
Technique
• Relates species
abundance to
environmental
variables
• Visualization of
variable
correlations
Problem Area
Average POV Energy
Application of CCA to Suburban
Travel Data
Counts of HH’s in Bin
Suburbs/Edge Cities
as Ecological
Communities
Average Daily VMT/bin
Average Values for
Suburban Variables
Example of CCA Capability:
Chicago VMT Data
Triplot
Careful: points are near centroid
Variables denote axes associations
Chicago Suburban VMT Data
..
.
..
.
.. ..
. .
Triplot coordinates show hierarchy of
variables in Chicago:
Demographic, Urban-form, Employment
Chicago Suburban VMT Data
Quad 2
Quad 1
Quad 3
Quad 4
Chicago Suburban VMT Data:
Focus on Quadrants
Quadrant 1
Quadrant 1:
Quadrant 2:
Mid1
Mid 2
1: Wilmette, Winnetka
Employment
Demographics
2: Skokie
Quadrant 3:
Quadrant 4:
Hi
Lo
Urban form
Residential
Density
3: Evanston, Homewood, Melrose
Park, Oak Lawn, Park Forest
Wilmette, Winnetka,
Evanston, and Skokie
spatially close, but
differ in demographic
and urban-form
variables
1: Wilmette, Winnetka
2: Skokie
3: Evanston,
Homewood, Melrose
Park, Oak Lawn, Park
Forest
Quadrant 1
Quad 2:
Mid 2
Demographics
Quad 1:
Mid1
Employment
Quad 4:
Lo
Residential
Density
Quad 3:
Hi
Urban
form
Conclusions For Part 4:
Targeting the Suburbs
• CCA enables visualization of the complex
relationship between variables, driving
behavior, and location Æ focused policy
– Identify location of similar suburbs, begin to
identify characteristics associated with subtle
differences
– Determine what variables have what degree of
effect on which suburbs and driving groups
– Low/high degrees of driving impacted most by
urban-form variables
– Moderate driving impacted by demographics
– Identify spatial boundaries between similar
and dissimilar groups of suburbs
Overall Conclusions
• Spatial analysis:
– Provided macro/microscopic views of driving
behavior; motivated detailed analysis
– Low efficiency vehicles use in suburbs
• Scenario test:
– Technology: Increasing fuel efficiency
– Behavior: Using current provision of rail and
settlement patterns could yield significant
savings
• New use/application of statistical methods reveal
complex variable interactions
– Different variables impact driving in different
locations
– Enables more targeted, effective policy
Acknowledgements
Funding:
US Department of Education GAANN
Diversifying Faculty in Illinois Fellowship
Transportation Center Dissertation
Fellowship
ARCS Fellowship
Advisors:
Kimberly Gray
Senior Transportation
Graduate Students:
Joseph Schofer
Emily Kushto
Pablo Durango-Cohen
Elaine Croft
Laurence Audenaerd
CMAP and Affiliates:
Kermit Wies
Ron Eash
Marcelo Lascano
Gray Group
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