Lecture 7
Stated Preference Methods
Cinzia Cirillo
1
Preference data
• Revealed Preferences RP
Respondents are questioned about what
they actually do.
RP data contain information about
current market equilibrium.
Historically economists rely on real
market data because a classical
concept affirms that only RP data have
thus and such properties to estimate
demand equations consistent with
market behavior.
• Stated preferences SP
Respondents are faced to hypothetical
choice situations.
SP data provides insights into problems
involving shifts in technological
frontiers.
There are many situations in which
analysts and researchers have little
alternative to take consumers at their
world or do nothing.
2
Why SP data?
• Organizations need to estimate demand for new products with new attributes
or features.
By definition, such applications have no RP data on which to rely, managers
face the choice of guessing or relying on well-designed and executed SP
research.
• Explanatory variables have little variability in the marketplace.
Even if products have been in the market for many years, it is not uncommon
for there to be little or no variability in key explanatory variables.
• Explanatory variables are highly collinear in the marketplace.
Cost and Time correlation. Technology constraints.
• New variables are introduced that now explain choices.
As a product categories grow and mature, new product features are
introduced and/or new designs supplant obsolete ones.
3
• Observational data cannot satisfy model assumptions and/or contain statistical
“nasties” which lurk in real data.
All models are only as good as their maintained assumptions. RP data may be of little
value when used to estimate the parameters assumptions.
• Observational data are time consuming and expensive to collect.
Very often RP data are expensive to obtain and may take considerable time to collect.
For example panel data involve observations of behavior at multiple points in time for
the same or independent samples of individuals.
• The product is not traded in the real market.
Many goods are not traded in real economic markets; for example, environmental
goods, public goods such as freeways or stadia. Yet society and its organizations often
require that they be valued, their costs and benefits calculated.
4
RP data typically:
• Depict the world as it is now (current
market equilibrium).
• Possess inherent relationship
between attributes (technological
constraints are fixed).
• Have only existing alternatives as
observables.
• Embody market and personal
constraints on the decision maker.
• Have high reliability and face validity,
• Yield one observation per
respondent at each observation
point.
SP data typically:
• Describe hypothetical or virtual decision
contexts (flexibility).
• Control relationship between attributes,
which permits mapping of utility functions
with technologies different from existing
ones.
• Can include existing and/or propose and/or
generic (unbranded or unlabelled) choice
alternatives.
• Cannot easily (in some cases cannot at all)
represent changes in market and personal
constraints effectively.
• Seem to be reliable when respondents
understand, are committed and can respond
to tasks.
• Usually yield multiple observations per
respondent at each observation point.
5
Preferences
1.
2.
3.
4.
Discrete choice of one option from a set of competing ones. This response
measures the most preferred option relative to the remaining, but provides no
information about the relative preferences among the non-chosen. That is a true
nominal scale.
‘Yes, I like this option’ ‘No, I don’t like this option’. This response clearly separates
alternatives into liked and not liked options and provides preferences.
Complete ranking of options from most to least preferred. This response orders all
options on a preference continuum, but provides no information about degree of
preference, no order.
Rating options on a scale. Expresses degrees of preference for each option by rating
them on a scale or responding via other psychometric methods such as magnitude
estimation. If the consumers can supply valid and reliable estimates of their degree
of preference this response contains information about equality, order and degrees
of differences and magnitude.
6
Discrete choice of one option from a set of competing ones
Auto > bus, train, ferry, carpool and
bus = train = ferry = carpool
Mode for journey to work
Take bus
Take train
Take ferry
Drive own auto
Carpool
Consumer chooses
X
7
‘Yes, I like this option’ ‘No, I don’t like this option’
• Auto > bus, train, ferry
• Carpool > bus, train, ferry
• Auto = carpool; bus = train = ferry
Mode for journey
to work
Take bus
Take train
Take ferry
Drive own auto
Carpool
Consumer will consider (y/n)
No
No
No
Yes
Yes
8
Complete ranking of options from most to least preferred
•
•
•
•
Auto > bus, train, ferry, carpool
Carpool > bus, train, ferry
Ferry > bus, train
Train > bus
Mode for journey
to work
Take bus
Take train
Take ferry
Drive own auto
Carpool
Ranking by
likelihood of use
5
4
3
1
2
9
Expressing degrees of preference by rating options on a scale
Mode for journey
to work
Take bus
Take train
Take ferry
Drive own auto
Carpool
•
•
•
•
Consumer likelihood to use (y/n)
4
4
6
10
7
Auto > bus, train, ferry, carpool
Carpool > bus, train, ferry
Ferry > bus, train
Train = bus
10
Part II:
Experimental Design
11
Definitions
• An experiment involves the manipulation of a variable with one or more observations, taken
in response to each manipulated value of the variable.
• The manipulated variable is called “factor”, and the values manipulated are called “factor
levels”.
• Such variables are also referred to as independent or explanatory variables or “attributes”.
• Factorial designs are designs in which each level of each attribute is combined with every
level of all other attributes.
• The complete enumeration is called a “complete factorial” or a “full factorial”. Complete
factorial guarantees that all attribute effects of interest are truly independent.
12
Choice experiments consist of a sample of choice sets selected from the
universal set of all possible choice sets that satisfy certain statistical
properties.
There are two general types of choice experiments:
1. labelled (alternative-specific)
2. unlabbeled (generic)
There are two general ways to design choice experiments for both types:
1. Sequentially design alternatives and then design the choice sets into which
there are placed;
2. Simultaneously design alternatives and assign them to choice sets.
13
Multiple choice experiments
The objective of multiple choice experiments is to design alternatives and the
choice sets in which they appear, such that the effect can be estimated with
reasonable levels of statistical precision.
Multiple choice experiments:
1. There are more than two alternatives (two brands and non-choice, eight
brands, etc) and
2. Choice set sizes may vary (some sets with two brands, some with eight, etc.
Design issues involve the following types of alternatives: (a) labelled vs.
unlabelled; (b) generic vs. alternative-specific; (c) own vs cross-effects.
14
Designs for MNL models
• Design an initial set of P total alternatives (profiles) to create choice sets
containing one or more additional alternatives M.
• Make M-1 copies of the initial set of P total profiles, and place the M sets of
profiles in M separate urns. Randomly select one of the P profiles from each of
the M urns without replacement to construct a choice set of exactly M
alternatives, ensuring that none of the M profiles in the set are the same.
Continue this process until all P profiles in each urn have assigned to P total
choice sets of M alternatives.
15
• Improve the statistical efficiency of the first procedure by creating M different,
statistically equivalent designs. In this case each urn contains a different design.
When one randomly draws profiles from the M urns to make the P total choice
sets, one does not have to eliminate duplicate profiles.
• Further improve design efficiency by first constructing the P total profiles and
then constructing the P total choice sets by a method known as shifting, in
which modular arithmetic is issued to shift each combination of the initial
attribute levels by adding a constant that depends on the number of levels.
• Make P initial profiles and construct all possible pairs of each. There will be
exactly P(P-1)/2 pairs. The total number of pairs will increase geometrically
with P.
16
Designs for availability problems
• Many problems involve sets of alternatives that vary in nature and
composition. In transport, it is rare for commuters to have all transport modes
available for their commuters. If IID is satisfied, label specific intercepts for J-1
alternatives can be estimated by designing this type of experiments.
• Each of the J labels can be treated as a two level variable (present/absent). A
nearly optimally efficient strategy is to design the choice sets using a 2J
fractional factorial design.
• If IID is violated a minimum strategy is to design the smallest orthogonal a 2J
main effects plus its foldover (a mirror image of the original design; replace
each 0 with 1 and each 1 with 0).
17
Set
United
Delta
Northwest
US Airways
Southwest
1
P
P
P
P
P
2
P
P
A
P
A
3
P
A
P
A
A
4
P
A
A
A
P
5
A
P
P
A
P
6
A
P
A
A
A
7
A
A
P
P
A
8
A
A
A
P
P
18
• Each airlines appears equally often (count the number of A and P in each
column).
• The presence/absence of each airline in independent of the presence/absence
of other airline.
Airline A
Airline B
Present
Absent
Present
2
2
Absent
2
2
• If two events are probabilistically independent their joint probabilities should
equal the product of their marginals (4x4)/8 = 2. The correlation of the cooccurrances is exactly zero.
19
• The marginal for each airline can be estimated independently of the marginals
of every other airline.
• The marginal of each airline is the best estimate of the alternative-specific
intercept or constant in MNL model.
• Alternative-specific intercepts can be estimated from several data aggregation
levels, and each will yield the same coefficients up to a multiplication by a
positive constant.
• The more one aggregate data, the more one hides individual and choice set
variation.
• Thus it is particularly dangerous to aggregate data over subjects because
consumers typically exhibits heterogeneous preferences.
20
Unlabelled, generic alternatives
The choice outcomes are purely generic in the sense that the labels attached to each option convey
no information beyond that provided by the attributes.
Options are simply labelled “A” and “B”.
Option A
Option B
Set
Fare
Service
Time
Fare
Service
Time
1
2
3
4
5
6
7
8
$1.20
$1.20
$1.20
$1.20
$2.20
$2.20
$2.20
$2.20
5
5
15
15
5
5
15
15
10
20
10
20
10
20
10
20
$ 2.00
$ 2.00
$ 3.00
$ 3.00
$ 3.00
$ 3.00
$ 2.00
$ 2.00
15
30
30
15
30
15
15
30
15
30
30
15
15
30
30
15
21
M = total generic choice outcomes
A = total attributes
L = levels for each attribute
The collective design is an LMA factorial, from which one selects the smallest
orthogonal main effects plan.
For example, if there are four choice outcomes, and each is described by eight
four level attributes, the collective factorial is 48x4, or 432. The smallest possible
main effect plan is determined by the total degrees of freedom required to
estimate all implied main effects.
The total degrees of freedom are determined by assuming the separate degree of
freedom in each main effect.
Each main effect has exactly L - 1 degree of freedom (= 3 in the present example).
22
There are 32 main effects (4 x 8 attributes); hence there is a total of 32 x 3, or 96
degrees of freedom. The smallest orthogonal main effects plan requires 128
choice sets.
Unbalanced designs are those for which
• Attributes have unequal numbers of levels
• The numbers of levels are not multiples of one another.
Hensher and al. say:
For example if three attributes have levels, respectively of 2, 3 and 4 the design
properties will be unbalanced. If the tree-level attribute can be reduced to two
or increased to four levels, design properties will be improved.
23
No of options
No of attributes
No of levels
Full factorial
Smallest design
2
2
2
2
4
4
4
4
8
8
8
8
16
16
4
4
8
16
4
4
8
16
4
4
8
16
4
8
2
4
2
4
2
4
2
4
2
4
2
4
2
4
28
48
216
432
216
416
232
464
232
432
264
4128
264
4128
16 sets
32 sets
32 sets
128 sets
32 sets
64 sets
64 sets
256 sets
64 sets
128 sets
128 sets
512 sets
128 sets
512 sets
24
Labelled alternatives
The design principle for unlabelled alternatives also apply to designs for labelled
alternatives.
The key difference is that the label or name of the alternative itself conveys
information to decision makers.
This matters in choice decisions because:
• Subjects may use labels to infer missing (omitted) information;
• These inferences may be (and usually are) correlated with the random
components.
The omitted variable bias can be quite serious.
For example, significant differences in price effects will occur to the extent that
consumers associate good or bad omitted variables with brands.
25
Good inferences lead to apparently lower price sensitivity, whereas bad inferences
lead to higher price sensitivity.
Such apparent effects are driven by failure to include in the task all the relevant
information on which consumers base their choices.
Models estimated from such tasks will be of limited value for future forecasting if
the covariance structure of the omitted variables changes.
Such changes should be slower in established, mature product markets, but may
be rapid in new and emerging markets.
26
Statistical properties of labelled choice experiments
Two statistical properties are of interest in labelled and unlabelled choice
experiments:
• Identification, that refers to the type of utility and choice process specifications
that can be estimated;
• Precision, that refers to the statistical efficiency of the parameters estimated
from the experiment.
Specification is, in principle, under the researcher’s control.
In practice, an experiment may be too large for practical application.
The real issue is precision, that is a function of the number of non-zero attributes
level differences (continuous attributes) or contrasts (qualitative attributes).
27
Difference design
• Difference designs requires one to begin with an initial set of profiles. An
additional M choice alternatives can be designed by using an orthogonal
difference design.
• Let all attributes be quantitative and let L = 4. Let the levels of each attribute in
the difference design be -3 -1 +1 +3.
• If the original price levels are $5, $7, $9, $11,
• The price levels of the second alternative would be:
• 5±1,3; 7±1,3; 9±1,3; 11±1,3; ($2, $4, $6, $8, $10, $12, $14)
• The resulting design will be orthogonal in its attribute level differences, but
will not be orthogonal in the absolute attribute levels.
28
A labeled experiment with constant third option
• All attribute columns of all alternatives are treated as a collective factorial, and a
constant, reference alternative is added to each choice set. Given M options, each
described by A attributes with L level, the collective factorial is an LMA. One selects
the smallest orthogonal design from this factorial that satisfies the desired
identification properties. Each choice set is a row in this fractional factorial design
matrix to which a constant is added. The constant can be a fixed attribute profile or
an option such as “no choice”. The subtraction of a constant from each attribute
column leaves design orthogonality unaffected.
29
Constant reference alternative is added to each choice set
One selects the smallest orthogonal design from this factorial that satisfies the
desired identification properties. Each choice set is a row in this fractional
factorial design matrix to which a constant is added.
This strategy has limitations:
1. A significant number of between-alternative attribute differences will be zero.
2. Some choice sets will contain dominant alternatives
3. Relatively large number of choice sets will be required.
30
Example of a labeled design and resulting attributes differences
26 factorial; six attributes each with 2 levels of variations
two zero differences; correlation service frequency- travel time = 0.474
Commuter train
set
1-way
City bus
Freq
Time
1-way
Freq
Attribute differences
Time
1-way
Freq
Time
1
$1.20
5
10
$2.00
15
15
-0.80
-10
-5
2
$1.20
5
20
$2.00
30
30
-0.80
-25
-10
3
$1.20
15
10
$3.00
30
30
-1.80
-15
-20
4
$1.20
15
20
$3.00
15
15
-1.80
0
+5
5
$2.20
5
10
$3.00
30
15
-0.80
-25
-5
6
$2.20
5
20
$3.00
15
30
-0.80
-10
+5
7
$2.20
15
10
$2.00
15
30
+0.20
0
-5
8
$2.20
15
20
$2.00
30
15
+0.20
-15
-10
31
A labeled experiment with constant third option
Commuter train
set
1-way
City bus
Freq
Time
1-way
Freq
Option
Time
1
$1.20
5
10
$2.00
15
15
2
$1.20
5
20
$2.00
30
30
3
$1.20
15
10
$3.00
30
30
4
$1.20
15
20
$3.00
15
15
5
$2.20
5
10
$3.00
30
15
6
$2.20
5
20
$3.00
15
30
7
$2.20
15
10
$2.00
15
30
8
$2.20
15
20
$2.00
30
15
Choose another mode of
travel to work
32
Attributes level differences resulting from random design
• Use separate designs to make profiles for train and bus, put the bus and the train
profiles in two different urns and generate pairs by randomly selecting a profile from
each urn without replacement.
• In this case there are no zero differences and correlation between service frequency
and travel time differences is 0.16. This randomly generated design is more efficient
that an orthogonal design but this cannot be generilazed.
33
Attributes level differences resulting from random design
23 x 23 factorial;
no zero differences; correlation service frequency- travel time = 0.16
Commuter train
set
1-way
City bus
Freq
Time
1-way
Freq
Attribute differences
Time
1-way
Freq
Time
1
$1.20
5
10
$3.00
15
30
-1.80
-10
-20
2
$1.20
5
20
$2.00
15
30
-0.80
-10
-10
3
$1.20
15
10
$3.00
30
15
-1.80
-15
-5
4
$1.20
15
20
$2.00
30
15
-0.80
-15
+5
5
$2.20
5
10
$2.00
15
15
+0.20
-10
-5
6
$2.20
5
20
$3.00
15
15
-0.80
-10
+5
7
$2.20
15
10
$2.00
30
30
+0.20
-15
-20
8
$2.20
15
20
$3.00
30
30
-0.80
-15
-10
34
Availability designs for labelled alternatives
Sometimes we need to generate designs with choice sets of variable size. This
applies to the following situations:
Out of stock. How do supply interruptions or difficulties affect choices?
Closure or service interruptions. How to travelers change their behavior when a
bridge or a road is closed?
New product introductions. How do choices change in response to new entrants
that may or may not be included?
Retention/switching. How do choices change in response to systematic changes in
availability?
This is very well adapted to study dynamics in behavior.
35
In the case in which presence/absence of options varies but not attributes, designs
can be created by treating alternatives as two level factors (present/absent) and
selecting orthogonal fractions from the 2J factorial.
Set
1
2
3
4
5
6
7
8
Option1
P
P
P
P
A
A
A
A
Option 2
A
A
P
P
A
A
P
P
Option 3
P
A
P
A
P
A
P
A
Option 4
A
A
P
P
P
P
A
A
Option 5
P
A
A
P
P
A
A
P
Option 6
A
P
P
A
P
A
A
P
36
Alternatives vary in availability and attributes
• Two design approaches are possible:
1. An orthogonal fraction of a 2J design is used to design presence/absence conditions
and designed attributes profiles are randomly assigned without replacement to
make choice in each condition.
2. A fraction of a 2J design is used to design presence/absence conditions, and a
second orthogonal fraction of the collective factorial of the attributes of the
alternative “present” is used to make the choice sets in each present/absent
condition
37
Attribute availability nesting based on fractional design
Set no.
A
B
C
Condition 1 (011): based on the smallest fraction of the 26
1
A
000
000
2
A
001
011
3
A
010
111
4
A
011
100
5
A
100
101
6
A
101
110
7
A
110
010
8
A
111
001
38
Set no.
A
B
C
Condition 2 (101): based on the smallest fraction of the 26
1
000
A
000
2
001
A
011
3
010
A
111
4
011
A
100
5
100
A
101
6
101
A
110
7
110
A
010
8
111
A
001
39
Set no.
A
B
C
Condition 3 (110): based on the smallest fraction of the 26
1
000
000
A
2
001
011
A
3
010
111
A
4
011
100
A
5
100
101
A
6
101
110
A
7
110
010
A
8
111
001
A
40
Overview
• Will present a few examples of stated preference surveys
– Maryland Vehicle Preference Survey
– Capitol Beltway HOT Lane Study
• Show survey progression from trial to first run for vehicle
preference survey with focus on new Fuel Technology
Experiment
• Focus on Departure Time Experiment for HOT study
41
Maryland Vehicle Preference Survey
Sources (abbreviated)
Cirillo, C. and Maness, M. Estimating Demand for New Technology Vehicles.
ETC 2011
Maness, M. and Cirillo, C. Measuring and Modeling Future Vehicle
Preferences: A Preliminary Stated Preference Survey in Maryland.
forthcoming
42
Objective
• Objectives
– Collect data on future household vehicle preferences in Maryland in
relation to vehicle technology, fuel type, and public policy
– Determine if respondent could make dynamic vehicle purchase
decisions in a hypothetical short- to medium-term period
– Determine if results from this hypothetical survey could be modeled
using discrete choice methods
43
Survey Design
• Respondent and Household Information
• Current Vehicle Properties
• Stated Preference Survey
– One of the following:
• Vehicle Technology Experiment
• Fuel Type Experiment
• Taxation Policy Experiment
44
Survey Methodology
Time Frame
Target Population
Sampling Frame
Sample Design
Use of Interviewer
Mode of Administration
Computer Assistance
Reporting Unit
Time Dimension
Frequency
Levels of Observation
Summer – Fall 2010
Suburban and Urban Maryland Households
Households with internet access in 5 Maryland counties
Multi-stage cluster design by county and zipcode
Self-administered
Self-administered via the computer and internet for remaining respondents
Computer-assisted self interview (CASI) and web-based survey
One person age 18 or older per household reports for the entire household
Cross-sectional survey with hypothetical longitudinal stated preference
experiments
One two-month phase of collecting responses
Household, vehicle, person
45
Experiment Directions
• Make realistic decisions. Act as if you were actually buying a vehicle in a
real life purchasing situation.
• Take into account the situations presented during the scenarios. If you
would not normally consider buying a vehicle, then do not. But if the
situation presented would make you reconsider in real life, then take them
into account.
• Assume that you maintain your current living situation with moderate
increases in income from year to year.
• Each scenario is independent from one another. Do not take into account
the decisions you made in former scenarios. For example, if you purchase
a vehicle in 2011, then in the next scenario forget about the new vehicle
and just assume you have your current real life vehicle.
46
Vehicle Technology Experiment
47
Results - Vehicle Technology
Vehicle Price vs Adoption Rate
25%
40000
35000
20%
25000
15%
20000
10%
15000
10000
5%
5000
0%
0
2010
2011
New gasoline
2012
New Hybrid
New Electric
2013
Gasoline Price
2014
Hybrid Price
2015
Electric Price
48
Vehicle Price
Adoption Rate
30000
Results – Vehicle Technology
ASC – New Gasoline Vehicle
ASC – New Hybrid Vehicle
ASC – New Electric Vehicle
Purchase Price [$10,000]
Fuel Economy Change [MPG] (current veh. MPG known)
Fuel Economy Change [MPG] (current veh. MPG unknown)
Recharging Range [100 miles]
Current Vehicle Age – Purchased New [yrs]
Current Vehicle Age – Purchased Used [yrs]
Minivan Dummy interacted with Family Households
SUV Dummy interacted with Family Households
Non-Electric Vehicle Error Component (standard deviation)
Non-Hybrid Vehicle Error Component (standard deviation)
Vehicle Size (mean)
Vehicle Size (standard deviation)
Likelihood with Zero Coefficients
Likelihood with Constants Only
Final Value of Likelihood
BEV
HEV
Gasoline
Coefficient
Current
Included in Utility























-1379.4 "Rho-Squared"
-1088.1 Adjusted "Rho-Squared"
-819.6 Number of Observations






Value
T-stat
-1.320
-1.760
-3.450
-0.639
0.039
-0.002
0.909
-0.123
-0.059
1.410
1.900
2.400
2.150
-0.435
1.09
-3.28
-2.93
-5.70
-5.42
2.68
-0.21
4.37
-4.34
-2.02
2.75
4.77
6.00
6.71
-2.42
6.61
0.406
0.395
995 (83)
49
Results – Vehicle Technology
• Gasoline and hybrid vehicles have a similar inherent preference
• Families influenced by vehicle size
• Fuel economy not significant for respondents who did not know
their own vehicle’s fuel economy
• Covariance between Vehicle Types
–
–
–
–
current vehicle + new gasoline vehicle (largest cov.)
new gasoline or current vehicle + new hybrid vehicle
new gasoline or current vehicle + new electric vehicle
new hybrid vehicle + new electric vehicle (smallest cov.)
• About 65% of respondents preferred smaller vehicles
50
Fuel Type Experiment
51
Results – Fuel Type
Fuel Price vs Adoption Rate
7
30%
5
Adoption Rate
20%
4
15%
3
10%
2
5%
1
0
0%
2010
New Gasoline
2011
New Alternative Fuel
2012
New Electric
2013
New Plug-In Hybrid
Gasoline Price
2014
Alternative Fuel Price
2015
Electricity Price
52
Price per Gallon (or Equivalent)
6
25%
Results – Fuel Type
ASC – New Gasoline Vehicle
ASC – New Alternative Fuel Vehicle
ASC – New Diesel Vehicle
ASC – New Battery Electric Vehicle
ASC – New Plug-in Hybrid Electric Vehicle
Fuel Price [$]
Gasoline Price – PHEV [$]
Electricity Price – BEV [$]
Electricity Price – PHEV [$]
Charge Time – BEV [hrs]
Charge Time – PHEV [hrs]
Average Fuel Economy [MPG, MPGe]
Current Vehicle Age – Purchased New [yrs]
Current Vehicle Age – Purchased Used [yrs]
Current Vehicle Error Component (standard deviation)
Electric Vehicle Error Component (standard deviation)
Liquid Fuel Vehicle Error Component (standard deviation)
Likelihood with Zero Coefficients
Likelihood with Constants Only
Final Value of Likelihood
PHEV
BEV
Diesel
AFV
Gasoline
Current
Coefficient
Included in Utility


























-901.3 "Rho-Squared"
-667.7 Adjusted "Rho-Squared"
-443.6 Number of Observations

Value
-8.810
-9.940
-10.300
-9.230
-10.100
-1.160
-0.358
-0.762
-0.569
-0.917
-0.164
0.039
-0.395
-0.377
2.290
2.300
3.460
T-stat
-6.81
-7.66
-7.84
-4.07
-4.79
-7.79
-2.02
-3.02
-2.79
-3.68
-0.87
3.91
-4.21
-3.86
3.90
3.92
4.91
0.508
0.489 53
503 (42)
Results – Fuel Type
• Respondents less sensitive to electricity price
– Maybe lack of familiarity, no rule of thumb?
• Charging time has influence on attractiveness of BEVs but not
PHEVs
• Error components shows that groups of respondents may have
similar propensity towards electric vehicles (BEV and PHEV)
and between liquid fuel vehicles
54
Taxation Policy Experiment
55
Results – Taxation Policy
VMT Tax vs Adoption Rate
35%
80
30%
70
Adoption Rate
50
20%
40
15%
30
10%
VMT Tax ($/1000 miles)
60
25%
20
5%
10
0%
0
2010
Drive Current Vehicle Less
2011
New Gasoline
2012
New Hybrid
New Electric
2013
Current Vehicle VMT
2014
Gasoline VMT
2015
Hybrid VMT
Electric VMT
56
Results – Taxation Policy
ASC – New Gasoline Vehicle
ASC – New Hybrid Vehicle
ASC – New Electric Vehicle
Hybrid Vehicle Deduction [$] divided by HH Income [$1000]
Electric Vehicle Deduction [$] divided by HH Income [$1000]
VMT Tax interacted with Annual Mileage [$100]
Toll Discount [%] (for HHs near toll facilities)
Toll Discount [%] (for HHs not near toll facilities)
Current Vehicle Age (new) interacted with Annual Mileage [years x 1000 miles]
Current Vehicle Age (used) interacted with Annual Mileage [years x 1000 miles]
New Vehicle Error Component (standard deviation)
Current Vehicle Error Component (fixed to 0)
BEV
HEV
Gasoline
Current
Coefficient
Likelihood with Zero Coefficients
Likelihood with Constants Only
Final Value of Likelihood
Included in Utility



















-565.6 "Rho-Squared"
-456.7 Adjusted "Rho-Squared"
-308.1 Number of Observations
Value
T-stat
-7.170
-7.090
-7.590
0.093
0.245
-0.186
0.065
0.005
-0.049
-0.026
3.760
0.000
-6.03
-5.94
-6.17
2.71
2.02
-5.14
2.76
0.75
-5.24
-2.47
4.90
Fixed
0.455
0.436
408 (34)
57
Results – Taxation Policy
• ASCs similar to Vehicle Technology Experiment
• Toll discount only significant for residents near toll facilities
• Higher VMT tax for gasoline vehicles dissuaded new gasoline
vehicle purchases
58
Survey Redesign
• Eliminate the taxation policy experiment
– Incorporate VMT tax into fuel type experiment
– Incorporate Rebates into vehicle technology experiment
• Added open-ended questions for purchase reason of current
vehicles
– Able to elicit some opinions about vehicle preferences, attitudes, and
concerns
• All respondents participate in both choice experiments
59
Survey Redesign
• Vehicle Technology Experiment
– Incorporate MPGe into vehicle technology experiment
• Respondents able to compare mpge and mpg in fuel technology experiment
well
– Added fees and rebates for different vehicle types
– Added Plug-in Hybrid Vehicle (PHEV) alternative
• Fuel Technology Experiment
– Removed diesel vehicle option, added flex-fuel vehicle option
– Added VMT tax depending on fuel type
60
New Vehicle Technology Experiment
61
New Fuel Type Experiment
62
New Fuel Type Experiment
• Purpose
– Collect data on future household vehicle preferences in Maryland in
relation to fuel type
– Determine if respondent could make dynamic vehicle purchase
decisions in a hypothetical short- to medium-term period
• Respondents given a stated preference survey over a
hypothetical five year period with two scenarios per year
63
Prior Data Collection
• Respondent Characteristics
– Age, gender, employment, commute
• Household Characteristics
– Size, children, workers, location
• Current Vehicle Characteristics
– Make and model, fuel economy, purchase reason
64
Alternatives
•
•
•
•
•
•
•
Keep Current Vehicle
Buy New Gasoline Vehicle
Buy New Alternative Fuel Vehicle
Buy New Flex-Fuel Vehicle
Buy New Battery Electric Vehicle
Buy New Plug-in Hybrid Vehicle
Sell Current Vehicle
65
Attributes
•
•
•
•
•
Fuel Price – $ per gallon (equivalent)
Miles Traveled Fee – $ per 1000 miles
Average Fuel Economy – miles per gallon (equivalent)
Fueling Station Availability – distance from home in miles
Battery Charging Time – hours per charge
66
Attribute Levels
•
•
•
•
•
•
6134 design
Fuel Price – 6 levels
Miles Traveled Fee – 3 levels
Average Fuel Economy – 3 levels
Fueling Station Availability – 3 levels
Battery Charging Time – 3 levels
67
Attribute Levels
2011
Fuel Cost
2.50
2.75
3.00
Gasoline Fuel
3.50
4.00
4.50
2.25
2.48
2.70
Alternative Fuel (E85)
3.15
3.60
4.05
3.70
4.40
4.90
Electricity
5.30
5.70
6.05
VMT
MPG
20
25
30
16
21
26
60
80
100
Avail /
Charge Fuel Cost
5
2.75
5
3.06
5
3.35
3.91
4.48
5.05
50
2.48
25
2.75
15
3.01
3.52
4.03
4.54
4
3.81
5
4.58
6
5.15
5.62
6.10
6.53
Attribute levels for first three years of the experiment
2012
VMT
MPG
22
28
34
18
24
30
65
85
105
Avail /
Charge Fuel Cost
5
3.03
5
3.41
5
3.73
4.37
5.02
5.66
50
2.72
25
3.07
15
3.36
3.93
4.52
5.10
4
3.93
5
4.76
6
5.40
5.96
6.53
7.06
2013
VMT
1.80
3.00
4.50
MPG
24
31
38
Avail /
Charge
5
5
5
1.00
1.80
2.50
20
27
34
50
25
15
0.50
1.00
1.80
70
90
110
3
4
5
68
Experimental Design
Design #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Price
0
1
2
3
4
5
0
1
2
3
4
5
0
1
2
3
4
5
VMT Fee
0
2
1
1
0
2
1
0
2
2
1
0
2
1
0
0
2
1
Attribute
MPG
0
2
2
0
1
1
1
0
0
1
2
2
2
1
1
2
0
0
Availability
0
0
1
2
2
1
1
1
2
0
0
2
2
2
0
1
1
0
Charge Time
0
1
0
2
1
2
1
2
1
0
2
0
2
0
2
1
0
1
69
Preliminary Model (New Data)
70
Preliminary Results
71
Capitol Beltway HOT Lane Study
Estimating Drivers’ Willingness to Pay for HOT
Lanes on I-495 in Maryland
72
Overview
• Purpose
– Determine preferences for use of high-occupancy toll (HOT) lanes on
I-495 in Maryland
– Determine cost and time preferences as well as high-occupancy
vehicle preference
• Respondents given two experiments, both deal with lane
choice and the second has a departure time component
73
Prior Data Collection
• Recent Trip (via I-495) Information
– Passengers, Route Choice, Trip Purpose
– Preferred Departure Time, Arrival Time
– Actual Travel Time
– Trip Distance on Beltway (D)
– Actual Departure Time (DT), Arrival Time
– Shortest Travel Time on Beltway (TTmin)
– Longest Travel Time on Beltway(TTmax)
– Fuel Cost (FC)
74
Departure Time Experiment
75
Alternatives
•
•
•
•
Normal Lanes
HOT Lane without passenger (paid)
HOT Lane with passenger (free)
Use alternative route
76
Attributes
• 5431 design
• Some attribute levels change depending on time of trip
• Departure Time
• Travel Time
– Minimum Travel Time
– Travel Time Range
• Fuel Cost
• Toll Cost
77
≥
Attribute Levels
Variable
Departure time
Minimum Travel Time
(minutes)
Normal Lane
HOT Lane
HOV Lane (passengers 2)
DT-40min
DT-40min
DT-40min
DT-20min
DT-20min
DT-20min
DT
DT
DT
DT+20min
DT+20min
DT+20min
DT+40min
DT+40min
DT+40min
TTmin
TTmin
TTmin
TTmin + 5
TTmin + 5
TTmin + 5
TTmin + 10
TTmin + 10
TTmin + 10
TTmin + 15
TTmin + 15
TTmin + 15
TTmin + 20
TTmin + 20
TTmin + 20
78
Attribute Levels
Variable
Travel Time Range
(minutes)
[during rush hour]
Travel Time Range
(minutes)
[not rush hour]
Normal Lane
HOT Lane
HOV Lane (passengers 2)
30
10
10
35
15
15
40
20
20
45
25
25
50
30
30
5
5
5
15
10
10
25
15
15
35
20
20
45
25
25
79
Attribute Levels
Variable
Toll Cost ($)
[during rush hour]
Toll Cost ($)
[not rush hour]
Normal Lane
HOT Lane
HOV Lane (passengers 2)
0
0.30 * D
0
0
0.35 * D
0
0
0.40 * D
0
0
0.45 * D
0
0
0.50 * D
0
0
0.10 * D
0
0
0.15 * D
0
0
0.20 * D
0
0
0.25 * D
0
0
0.30 * D
0
80
Attribute Levels
Variable
Fuel Cost
[during rush hour]
Fuel Cost
[not rush hour]
Normal Lane
HOT Lane
HOV Lane (passengers 2)
FC * 110%
FC
FC
FC * 120%
FC * 110%
FC * 110%
FC * 130%
FC * 120%
FC * 120%
FC * 110%
FC
FC
FC * 115%
FC * 115%
FC * 115%
FC * 120%
FC * 120%
FC * 120%
81
Experimental Design
Scenario #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Depart Time
0
0
0
0
0
1
1
1
1
1
2
2
2
2
2
3
3
3
3
3
4
4
4
4
4
Min TT
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
0
1
2
3
4
TT Range
0
2
3
4
1
1
4
0
3
2
2
3
1
0
4
3
1
4
2
0
4
0
2
1
3
Fuel Cost
0
1
2
1
2
1
0
1
2
2
2
1
0
2
1
1
2
2
0
1
2
2
1
1
0
Toll Cost
0
4
1
2
3
1
3
2
4
0
2
0
4
3
1
3
2
0
1
4
4
1
3
0
2
82