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. 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. 2. 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. 3. 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. 4. 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 Consumer chooses Take bus Take train Take ferry Drive own auto Carpool 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 2 J fractional factorial design. If IID is violated a minimum strategy is to design the smallest orthogonal a 2 J 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 L MA 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 4 8x4, or 4 32. 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 4 2 2 8 16 sets 2 4 4 4 8 32 sets 2 8 2 2 16 32 sets 2 16 4 4 32 128 sets 4 4 2 2 16 32 sets 4 4 4 4 16 64 sets 4 8 2 2 32 64 sets 4 16 4 4 64 256 sets 8 4 2 2 32 64 sets 8 4 4 4 32 128 sets 8 8 2 2 64 128 sets 8 16 4 4 128 512 sets 16 4 2 2 64 128 sets 16 8 4 4 128 512 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 L MA. 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 2 6 factorial; six attributes each with 2 levels of variations two zero differences; correlation service frequency- travel time = 0.474 Commuter train City bus Attribute differences set 1-way Freq Time 1-way Freq 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 City bus Option set 1-way Freq Time 1-way Freq Time Choose another mode of 1 $1.20 5 10 $2.00 15 15 travel to work 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 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 2 3 x 2 3 factorial; no zero differences; correlation service frequency- travel time = 0.16 Commuter train City bus Attribute differences set 1-way Freq Time 1-way Freq 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
36 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 2 J factorial. Set Option1 Option 2 Option 3 Option 4 Option 5 Option 6 1 2 3 4 5 6 7 8 P P P P A A A A A A P P A A P P P A P A P A P A A A P P P P A A P A A P P A A P A P P A P A A P
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 2 6 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 2 6 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 2 6 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 Summer Fall 2010 Target Population Suburban and Urban Maryland Households Sampling Frame Households with internet access in 5 Maryland counties Sample Design Multi-stage cluster design by county and zipcode Use of Interviewer Self-administered Mode of Administration Self-administered via the computer and internet for remaining respondents Computer Assistance Computer-assisted self interview (CASI) and web-based survey Reporting Unit One person age 18 or older per household reports for the entire household Time Dimension Cross-sectional survey with hypothetical longitudinal stated preference experiments Frequency One two-month phase of collecting responses Levels of Observation 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 25% Vehicle Price vs Adoption Rate 40000 20% 35000 30000 Adoption Rate 15% 10% 25000 20000 15000 Vehicle Price 5% 10000 5000 0% 2010 2011 2012 2013 2014 2015 New gasoline New Hybrid New Electric Gasoline Price Hybrid Price Electric Price 0 48
Results Vehicle Technology Coefficient Included in Utility Value T-stat ASC New Gasoline Vehicle -1.320-3.28 ASC New Hybrid Vehicle -1.760-2.93 ASC New Electric Vehicle -3.450-5.70 Purchase Price [$10,000] -0.639-5.42 Fuel Economy Change [MPG] (current veh. MPG known) 0.039 2.68 Fuel Economy Change [MPG] (current veh. MPG unknown) -0.002-0.21 Recharging Range [100 miles] 0.909 4.37 Current Vehicle Age Purchased New [yrs] -0.123-4.34 Current Vehicle Age Purchased Used [yrs] -0.059-2.02 Minivan Dummy interacted with Family Households 1.410 2.75 SUV Dummy interacted with Family Households 1.900 4.77 Non-Electric Vehicle Error Component (standard deviation) 2.400 6.00 Non-Hybrid Vehicle Error Component (standard deviation) 2.150 6.71 Vehicle Size (mean) -0.435-2.42 Vehicle Size (standard deviation) 1.09 6.61 Likelihood with Zero Coefficients -1379.4 "Rho-Squared" 0.406 Likelihood with Constants Only -1088.1 Adjusted "Rho-Squared" 0.395 Final Value of Likelihood -819.6 Number of Observations 995 (83) 49 Current Gasoline HEV BEV
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 30% Fuel Price vs Adoption Rate 7 25% 6 Adoption Rate 20% 15% 10% 5 4 3 2 Price per Gallon (or Equivalent) 5% 1 0% 2010 2011 2012 2013 2014 2015 New Gasoline New Alternative Fuel New Electric New Plug-In Hybrid Gasoline Price Alternative Fuel Price Electricity Price 0 52
Results Fuel Type Coefficient Current Gasoline Included in Utility AFV Diesel BEV PHEV Value T-stat ASC New Gasoline Vehicle -8.810-6.81 ASC New Alternative Fuel Vehicle -9.940-7.66 ASC New Diesel Vehicle -10.300-7.84 ASC New Battery Electric Vehicle -9.230-4.07 ASC New Plug-in Hybrid Electric Vehicle -10.100-4.79 Fuel Price [$] -1.160-7.79 Gasoline Price PHEV [$] -0.358-2.02 Electricity Price BEV [$] -0.762-3.02 Electricity Price PHEV [$] -0.569-2.79 Charge Time BEV [hrs] -0.917-3.68 Charge Time PHEV [hrs] -0.164-0.87 Average Fuel Economy [MPG, MPGe] 0.039 3.91 Current Vehicle Age Purchased New [yrs] -0.395-4.21 Current Vehicle Age Purchased Used [yrs] -0.377-3.86 Current Vehicle Error Component (standard deviation) 2.290 3.90 Electric Vehicle Error Component (standard deviation) 2.300 3.92 Liquid Fuel Vehicle Error Component (standard deviation) 3.460 4.91 Likelihood with Zero Coefficients -901.3 "Rho-Squared" 0.508 Likelihood with Constants Only -667.7 Adjusted "Rho-Squared" 0.489 53 Final Value of Likelihood -443.6 Number of Observations 503 (42)
Results Fuel Type Respondents less sensitive to electricity price Maybe lack of familiarity, no rule of thumb? Charging time has influence on attractiveness of BEVs but not PHEVs Error components shows that groups of respondents may have similar propensity towards electric vehicles (BEV and PHEV) and between liquid fuel vehicles 54
Taxation Policy Experiment 55
Results Taxation Policy 35% VMT Tax vs Adoption Rate 80 30% 70 25% 60 Adoption Rate 20% 15% 10% 50 40 30 20 VMT Tax ($/1000 miles) 5% 10 0% 0 2010 2011 2012 2013 2014 2015 Drive Current Vehicle Less New Gasoline New Hybrid New Electric Current Vehicle VMT Gasoline VMT Hybrid VMT Electric VMT 56
Results Taxation Policy Coefficient Included in Utility Value T-stat ASC New Gasoline Vehicle -7.170-6.03 ASC New Hybrid Vehicle -7.090-5.94 ASC New Electric Vehicle -7.590-6.17 Hybrid Vehicle Deduction [$] divided by HH Income [$1000] 0.093 2.71 Electric Vehicle Deduction [$] divided by HH Income [$1000] 0.245 2.02 VMT Tax interacted with Annual Mileage [$100] -0.186-5.14 Toll Discount [%] (for HHs near toll facilities) 0.065 2.76 Toll Discount [%] (for HHs not near toll facilities) 0.005 0.75 Current Vehicle Age (new) interacted with Annual Mileage [years x 1000 miles] -0.049-5.24 Current Vehicle Age (used) interacted with Annual Mileage [years x 1000 miles] -0.026-2.47 New Vehicle Error Component (standard deviation) 3.760 4.90 Current Vehicle Error Component (fixed to 0) 0.000 Fixed Likelihood with Zero Coefficients -565.6 "Rho-Squared" 0.455 Likelihood with Constants Only -456.7 Adjusted "Rho-Squared" 0.436 Final Value of Likelihood -308.1 Number of Observations 408 (34) Current Gasoline HEV BEV 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 6 1 3 4 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 Gasoline Fuel Alternative Fuel (E85) Electricity 2011 2012 2013 Fuel Cost VMT MPG Avail / Charge Fuel Cost VMT MPG Avail / Charge Fuel Cost VMT MPG Avail / Charge 2.50 20 5 2.75 22 5 3.03 1.80 24 5 2.75 25 5 3.06 28 5 3.41 3.00 31 5 3.00 30 5 3.35 34 5 3.73 4.50 38 5 3.50 3.91 4.37 4.00 4.48 5.02 4.50 5.05 5.66 2.25 16 50 2.48 18 50 2.72 1.00 20 50 2.48 21 25 2.75 24 25 3.07 1.80 27 25 2.70 26 15 3.01 30 15 3.36 2.50 34 15 3.15 3.52 3.93 3.60 4.03 4.52 4.05 4.54 5.10 3.70 60 4 3.81 65 4 3.93 0.50 70 3 4.40 80 5 4.58 85 5 4.76 1.00 90 4 4.90 100 6 5.15 105 6 5.40 1.80 110 5 5.30 5.62 5.96 5.70 6.10 6.53 6.05 6.53 7.06 Attribute levels for first three years of the experiment 68
Experimental Design Attribute Design # Price VMT Fee MPG Availability Charge Time 1 0 0 0 0 0 2 1 2 2 0 1 3 2 1 2 1 0 4 3 1 0 2 2 5 4 0 1 2 1 6 5 2 1 1 2 7 0 1 1 1 1 8 1 0 0 1 2 9 2 2 0 2 1 10 3 2 1 0 0 11 4 1 2 0 2 12 5 0 2 2 0 13 0 2 2 2 2 14 1 1 1 2 0 15 2 0 1 0 2 16 3 0 2 1 1 17 4 2 0 1 0 18 5 1 0 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 5 4 3 1 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 Normal Lane HOT Lane HOV Lane (passengers 2) Departure time Minimum Travel Time (minutes) 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 Normal Lane HOT Lane HOV Lane (passengers 2) Travel Time Range (minutes) [during rush hour] Travel Time Range (minutes) [not rush hour] 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 Normal Lane HOT Lane HOV Lane (passengers 2) Toll Cost ($) [during rush hour] Toll Cost ($) [not rush hour] 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 Normal Lane HOT Lane HOV Lane (passengers 2) Fuel Cost [during rush hour] Fuel Cost [not rush hour] 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 # Depart Time Min TT TT Range Fuel Cost Toll Cost 1 0 0 0 0 0 2 0 1 2 1 4 3 0 2 3 2 1 4 0 3 4 1 2 5 0 4 1 2 3 6 1 0 1 1 1 7 1 1 4 0 3 8 1 2 0 1 2 9 1 3 3 2 4 10 1 4 2 2 0 11 2 0 2 2 2 12 2 1 3 1 0 13 2 2 1 0 4 14 2 3 0 2 3 15 2 4 4 1 1 16 3 0 3 1 3 17 3 1 1 2 2 18 3 2 4 2 0 19 3 3 2 0 1 20 3 4 0 1 4 21 4 0 4 2 4 22 4 1 0 2 1 23 4 2 2 1 3 24 4 3 1 1 0 25 4 4 3 0 2 82