Lecture 7. Stated Preference Methods. Cinzia Cirillo

Size: px
Start display at page:

Download "Lecture 7. Stated Preference Methods. Cinzia Cirillo"

Transcription

1 Lecture 7 Stated Preference Methods Cinzia Cirillo 1

2 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

3 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

4 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

5 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

6 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

7 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

8 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

9 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

10 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) Auto > bus, train, ferry, carpool Carpool > bus, train, ferry Ferry > bus, train Train = bus 10

11 Part II: Experimental Design 11

12 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

13 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

14 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

15 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

16 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

17 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

18 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

19 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

20 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

21 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 $1.20 $1.20 $1.20 $1.20 $2.20 $2.20 $2.20 $ $ 2.00 $ 2.00 $ 3.00 $ 3.00 $ 3.00 $ 3.00 $ 2.00 $

22 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 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

23 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

24 No of options No of attributes No of levels Full factorial Smallest design sets sets sets sets sets sets sets sets sets sets sets sets sets sets 24

25 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

26 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

27 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

28 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 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

29 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

30 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

31 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 = Commuter train City bus Attribute differences set 1-way Freq Time 1-way Freq Time 1-way Freq Time 1 $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $

32 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 $ $ travel to work 2 $ $ $ $ $ $ $ $ $ $ $ $ $ $

33 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 This randomly generated design is more efficient that an orthogonal design but this cannot be generilazed. 33

34 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 $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $

35 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 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 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

37 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

38 Attribute availability nesting based on fractional design Set no. A B C Condition 1 (011): based on the smallest fraction of the A A A A A A A A

39 Set no. A B C Condition 2 (101): based on the smallest fraction of the A A A A A A A A

40 Set no. A B C Condition 3 (110): based on the smallest fraction of the A A A A A A A A 40

41 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

42 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

43 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

44 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

45 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

46 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

47 Vehicle Technology Experiment 47

48 Results - Vehicle Technology 25% Vehicle Price vs Adoption Rate % Adoption Rate 15% 10% Vehicle Price 5% % New gasoline New Hybrid New Electric Gasoline Price Hybrid Price Electric Price 0 48

49 Results Vehicle Technology Coefficient Included in Utility Value T-stat 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 "Rho-Squared" Likelihood with Constants Only Adjusted "Rho-Squared" Final Value of Likelihood Number of Observations 995 (83) 49 Current Gasoline HEV BEV

50 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

51 Fuel Type Experiment 51

52 Results Fuel Type 30% Fuel Price vs Adoption Rate 7 25% 6 Adoption Rate 20% 15% 10% Price per Gallon (or Equivalent) 5% 1 0% New Gasoline New Alternative Fuel New Electric New Plug-In Hybrid Gasoline Price Alternative Fuel Price Electricity Price 0 52

53 Results Fuel Type Coefficient Current Gasoline Included in Utility AFV Diesel BEV PHEV Value T-stat 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 "Rho-Squared" Likelihood with Constants Only Adjusted "Rho-Squared" Final Value of Likelihood Number of Observations 503 (42)

54 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

55 Taxation Policy Experiment 55

56 Results Taxation Policy 35% VMT Tax vs Adoption Rate 80 30% 70 25% 60 Adoption Rate 20% 15% 10% VMT Tax ($/1000 miles) 5% 10 0% Drive Current Vehicle Less New Gasoline New Hybrid New Electric Current Vehicle VMT Gasoline VMT Hybrid VMT Electric VMT 56

57 Results Taxation Policy Coefficient Included in Utility Value T-stat 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) Fixed Likelihood with Zero Coefficients "Rho-Squared" Likelihood with Constants Only Adjusted "Rho-Squared" Final Value of Likelihood Number of Observations 408 (34) Current Gasoline HEV BEV 57

58 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

59 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

60 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

61 New Vehicle Technology Experiment 61

62 New Fuel Type Experiment 62

63 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

64 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

65 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

66 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

67 Attribute Levels 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

68 Attribute Levels Gasoline Fuel Alternative Fuel (E85) Electricity Fuel Cost VMT MPG Avail / Charge Fuel Cost VMT MPG Avail / Charge Fuel Cost VMT MPG Avail / Charge Attribute levels for first three years of the experiment 68

69 Experimental Design Attribute Design # Price VMT Fee MPG Availability Charge Time

70 Preliminary Model (New Data) 70

71 Preliminary Results 71

72 Capitol Beltway HOT Lane Study Estimating Drivers Willingness to Pay for HOT Lanes on I-495 in Maryland 72

73 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

74 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

75 Departure Time Experiment 75

76 Alternatives Normal Lanes HOT Lane without passenger (paid) HOT Lane with passenger (free) Use alternative route 76

77 Attributes 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

78 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

79 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]

80 Attribute Levels Variable Normal Lane HOT Lane HOV Lane (passengers 2) Toll Cost ($) [during rush hour] Toll Cost ($) [not rush hour] * D * D * D * D * D * D * D * D * D * D 0 80

81 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

82 Experimental Design Scenario # Depart Time Min TT TT Range Fuel Cost Toll Cost

MEASURING AND MODELING FUTURE VEHICLE PREFERENCES: A PRELIMINARY STATED PREFERENCE SURVEY IN MARYLAND

MEASURING AND MODELING FUTURE VEHICLE PREFERENCES: A PRELIMINARY STATED PREFERENCE SURVEY IN MARYLAND 0 0 0 MEASURING AND MODELING FUTURE VEHICLE PREFERENCES: A PRELIMINARY STATED PREFERENCE SURVEY IN MARYLAND Michael Maness* Graduate Research Assistant University of Maryland Department of Civil and Environmental

More information

Vehicle Scrappage and Gasoline Policy. Online Appendix. Alternative First Stage and Reduced Form Specifications

Vehicle Scrappage and Gasoline Policy. Online Appendix. Alternative First Stage and Reduced Form Specifications Vehicle Scrappage and Gasoline Policy By Mark R. Jacobsen and Arthur A. van Benthem Online Appendix Appendix A Alternative First Stage and Reduced Form Specifications Reduced Form Using MPG Quartiles The

More information

Factors Affecting Vehicle Use in Multiple-Vehicle Households

Factors Affecting Vehicle Use in Multiple-Vehicle Households Factors Affecting Vehicle Use in Multiple-Vehicle Households Rachel West and Don Pickrell 2009 NHTS Workshop June 6, 2011 Road Map Prevalence of multiple-vehicle households Contributions to total fleet,

More information

Online Appendix for Subways, Strikes, and Slowdowns: The Impacts of Public Transit on Traffic Congestion

Online Appendix for Subways, Strikes, and Slowdowns: The Impacts of Public Transit on Traffic Congestion Online Appendix for Subways, Strikes, and Slowdowns: The Impacts of Public Transit on Traffic Congestion ByMICHAELL.ANDERSON AI. Mathematical Appendix Distance to nearest bus line: Suppose that bus lines

More information

2018 Automotive Fuel Economy Survey Report

2018 Automotive Fuel Economy Survey Report 2018 Automotive Fuel Economy Survey Report The Consumer Reports Survey Team conducted a nationally representative survey in May 2018 to assess American adults attitudes and viewpoints on vehicle fuel economy.

More information

Powertrain Acceptance & Consumer Engagement Study. Chrysler Powertrain Research March

Powertrain Acceptance & Consumer Engagement Study. Chrysler Powertrain Research March Powertrain Acceptance & Consumer Engagement Study Chrysler Powertrain Research March 2008 1 Research Objectives The 2010 Morpace Powertrain Acceptance & Consumer Engagement (PACE) study builds upon the

More information

More persons in the cars? Status and potential for change in car occupancy rates in Norway

More persons in the cars? Status and potential for change in car occupancy rates in Norway Author(s): Liva Vågane Oslo 2009, 57 pages Norwegian language Summary: More persons in the cars? Status and potential for change in car occupancy rates in Norway Results from national travel surveys in

More information

Parking Pricing As a TDM Strategy

Parking Pricing As a TDM Strategy Parking Pricing As a TDM Strategy Wei-Shiuen Ng Postdoctoral Scholar Precourt Energy Efficiency Center Stanford University ACT Northern California Transportation Research Symposium April 30, 2015 Parking

More information

A Techno-Economic Analysis of BEVs with Fast Charging Infrastructure. Jeremy Neubauer Ahmad Pesaran

A Techno-Economic Analysis of BEVs with Fast Charging Infrastructure. Jeremy Neubauer Ahmad Pesaran A Techno-Economic Analysis of BEVs with Fast Charging Infrastructure Jeremy Neubauer (jeremy.neubauer@nrel.gov) Ahmad Pesaran Sponsored by DOE VTO Brian Cunningham David Howell NREL is a national laboratory

More information

Missouri Seat Belt Usage Survey for 2017

Missouri Seat Belt Usage Survey for 2017 Missouri Seat Belt Usage Survey for 2017 Conducted for the Highway Safety & Traffic Division of the Missouri Department of Transportation by The Missouri Safety Center University of Central Missouri Final

More information

Investigation of Relationship between Fuel Economy and Owner Satisfaction

Investigation of Relationship between Fuel Economy and Owner Satisfaction Investigation of Relationship between Fuel Economy and Owner Satisfaction June 2016 Malcolm Hazel, Consultant Michael S. Saccucci, Keith Newsom-Stewart, Martin Romm, Consumer Reports Introduction This

More information

An Analytic Method for Estimation of Electric Vehicle Range Requirements, Electrification Potential and Prospective Market Size*

An Analytic Method for Estimation of Electric Vehicle Range Requirements, Electrification Potential and Prospective Market Size* An Analytic Method for Estimation of Electric Vehicle Range Requirements, Electrification Potential and Prospective Market Size* Mike Tamor Chris Gearhart Ford Motor Company *Population Statisticians and

More information

BUILDING A ROBUST INDUSTRY INDEX BASED ON LONGITUDINAL DATA

BUILDING A ROBUST INDUSTRY INDEX BASED ON LONGITUDINAL DATA CASE STUDY BUILDING A ROBUST INDUSTRY INDEX BASED ON LONGITUDINAL DATA Hanover built a first of its kind index to diagnose the health, trends, and hidden opportunities for the fastgrowing auto care industry.

More information

A Survey of Electric Vehicle Awareness & Preferences in Vermont

A Survey of Electric Vehicle Awareness & Preferences in Vermont A Survey of Electric Vehicle Awareness & Preferences in Vermont Research Conducted by The MSR Group September 2014 veic.org Tel: (802) 658-6060 Toll-free: (800) 639-6069 VEIC Headquarters: 128 Lakeside

More information

WHITE PAPER. Preventing Collisions and Reducing Fleet Costs While Using the Zendrive Dashboard

WHITE PAPER. Preventing Collisions and Reducing Fleet Costs While Using the Zendrive Dashboard WHITE PAPER Preventing Collisions and Reducing Fleet Costs While Using the Zendrive Dashboard August 2017 Introduction The term accident, even in a collision sense, often has the connotation of being an

More information

Philip Schaffner & Jason Junge Minnesota Department of Transportation

Philip Schaffner & Jason Junge Minnesota Department of Transportation Philip Schaffner & Jason Junge Minnesota Department of Transportation 100% 80% 60% 40% 20% 0% 9% 33% 9% 21% 29% Trunk Highways $1.3B 14% 16% 19% 33% 17% Greater Minnesota Transit $55.7M 25% 27% 36% Note:

More information

Written Exam Public Transport + Answers

Written Exam Public Transport + Answers Faculty of Engineering Technology Written Exam Public Transport + Written Exam Public Transport (195421200-1A) Teacher van Zuilekom Course code 195421200 Date and time 7-11-2011, 8:45-12:15 Location OH116

More information

SUMMARY OF THE IMPACT ASSESSMENT

SUMMARY OF THE IMPACT ASSESSMENT COMMISSION OF THE EUROPEAN COMMUNITIES Brussels, 13.11.2008 SEC(2008) 2861 COMMISSION STAFF WORKING DOCUMT Accompanying document to the Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMT AND OF THE COUNCIL

More information

Vehicle Miles (Not) Traveled: Why Fuel Economy Requirements Don t Increase Household Driving

Vehicle Miles (Not) Traveled: Why Fuel Economy Requirements Don t Increase Household Driving Vehicle Miles (Not) Traveled: Why Fuel Economy Requirements Don t Increase Household Driving Jeremy West: MIT Mark Hoekstra: Texas A&M, NBER Jonathan Meer: Texas A&M, NBER Steven Puller: Texas A&M, NBER,

More information

Investigating the Concordance Relationship Between the HSA Cut Scores and the PARCC Cut Scores Using the 2016 PARCC Test Data

Investigating the Concordance Relationship Between the HSA Cut Scores and the PARCC Cut Scores Using the 2016 PARCC Test Data Investigating the Concordance Relationship Between the HSA Cut Scores and the PARCC Cut Scores Using the 2016 PARCC Test Data A Research Report Submitted to the Maryland State Department of Education (MSDE)

More information

ESTIMATING ELASTICITIES OF HOUSEHOLD DEMAND FOR FUELS FROM CHOICE ELASTICITIES BASED ON STATED PREFERENCE

ESTIMATING ELASTICITIES OF HOUSEHOLD DEMAND FOR FUELS FROM CHOICE ELASTICITIES BASED ON STATED PREFERENCE ESTIMATING ELASTICITIES OF HOUSEHOLD DEMAND FOR FUELS FROM CHOICE ELASTICITIES BASED ON STATED PREFERENCE Zeenat ABDOOLAKHAN zabdoola@biz.uwa.edu.au, 08 6488 2908 Information Management and Transport School

More information

Consumer Choice Modeling

Consumer Choice Modeling Consumer Choice Modeling David S. Bunch Graduate School of Management, UC Davis with Sonia Yeh, Chris Yang, Kalai Ramea (ITS Davis) 1 Motivation for Focusing on Consumer Choice Modeling Ongoing general

More information

CHANGE IN DRIVERS PARKING PREFERENCE AFTER THE INTRODUCTION OF STRENGTHENED PARKING REGULATIONS

CHANGE IN DRIVERS PARKING PREFERENCE AFTER THE INTRODUCTION OF STRENGTHENED PARKING REGULATIONS CHANGE IN DRIVERS PARKING PREFERENCE AFTER THE INTRODUCTION OF STRENGTHENED PARKING REGULATIONS Kazuyuki TAKADA, Tokyo Denki University, takada@g.dendai.ac.jp Norio TAJIMA, Tokyo Denki University, 09rmk19@dendai.ac.jp

More information

Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran

Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran Statistics and Quantitative Analysis U4320 Segment 8 Prof. Sharyn O Halloran I. Introduction A. Overview 1. Ways to describe, summarize and display data. 2.Summary statements: Mean Standard deviation Variance

More information

CTR Employer Survey Report

CTR Employer Survey Report CTR Employer Survey Report Employer Id : E11056 City of Lacey Employer : Worksite : City of Lacey Street : 420 College St Se Jurisdiction : City of Lacey Thank you for completing your Commute Trip Reduction

More information

Electric Vehicle Consumer Survey

Electric Vehicle Consumer Survey RESEARCH REPORT Electric Vehicle Consumer Survey Consumer Attitudes, Opinions, and Preferences for Electric Vehicles and EV Charging Stations Published 4Q 2013 Charul Vyas Associate Analyst Dave Hurst

More information

Car Economics Activity

Car Economics Activity Car Economics Activity INTRODUCTION Have you, or someone you know, bought a car recently? What factors were taken into consideration in choosing the car? Make and model, safety, reliability, -- how cool

More information

Funding Scenario Descriptions & Performance

Funding Scenario Descriptions & Performance Funding Scenario Descriptions & Performance These scenarios were developed based on direction set by the Task Force at previous meetings. They represent approaches for funding to further Task Force discussion

More information

Area-Wide Road Pricing Research in Minnesota

Area-Wide Road Pricing Research in Minnesota Area-Wide Road Pricing Research in Minnesota Transportation Research Forum, 2006 Annual Forum, New York University Kenneth R. Buckeye, AICP Project Manager Office of Investment Management Minnesota Department

More information

Optimal Policy for Plug-In Hybrid Electric Vehicles Adoption IAEE 2014

Optimal Policy for Plug-In Hybrid Electric Vehicles Adoption IAEE 2014 Optimal Policy for Plug-In Hybrid Electric Vehicles Adoption IAEE 2014 June 17, 2014 OUTLINE Problem Statement Methodology Results Conclusion & Future Work Motivation Consumers adoption of energy-efficient

More information

CTR Employer Survey Report

CTR Employer Survey Report CTR Employer Survey Report Employer Id : E12740 WA State Dept. of Agriculture Employer : Worksite : Cleveland Lab Street : 3939 Cleveland Ave Se Jurisdiction : City of Olympia Thank you for completing

More information

Abstract. Executive Summary. Emily Rogers Jean Wang ORF 467 Final Report-Middlesex County

Abstract. Executive Summary. Emily Rogers Jean Wang ORF 467 Final Report-Middlesex County Emily Rogers Jean Wang ORF 467 Final Report-Middlesex County Abstract The purpose of this investigation is to model the demand for an ataxi system in Middlesex County. Given transportation statistics for

More information

The U.S. Auto Industry, Washington and New Priorities:

The U.S. Auto Industry, Washington and New Priorities: The U.S. Auto Industry, Washington and New Priorities: What Americans Think Produced for Civil Society Institute Prepared by November 20, 2006 Copyright 2006. Opinion Research Corporation. All rights reserved.

More information

Parking Management Element

Parking Management Element Parking Management Element The State Transportation Planning Rule, adopted in 1991, requires that the Metropolitan Planning Organization (MPO) area implement, through its member jurisdictions, a parking

More information

2 VALUE PROPOSITION VALUE PROPOSITION DEVELOPMENT

2 VALUE PROPOSITION VALUE PROPOSITION DEVELOPMENT 2 VALUE PROPOSITION The purpose of the Value Proposition is to define a number of metrics or interesting facts that clearly demonstrate the value of the existing Xpress system to external audiences including

More information

Size Matters: How Vehicle Body Type Affects Consumer Preferences for Electric Vehicles Body Type and EV Preferences

Size Matters: How Vehicle Body Type Affects Consumer Preferences for Electric Vehicles Body Type and EV Preferences Size Matters: How Vehicle Body Type Affects Consumer Preferences for Electric Vehicles Christopher Higgins Moataz Mohamed Mark Ferguson March 16, 2011 Introduction How to encourage EV use? Who to target,

More information

Fuel Consumption and Technological Progress in Chinese Automobile Sector. Yang Yu Stanford University (Working with Yang Shu and Yueming Lucy Qiu)

Fuel Consumption and Technological Progress in Chinese Automobile Sector. Yang Yu Stanford University (Working with Yang Shu and Yueming Lucy Qiu) Fuel Consumption and Technological Progress in Chinese Automobile Sector Yang Yu Stanford University (Working with Yang Shu and Yueming Lucy Qiu) Outline Background China s Automobile Market and Fuel Consumption

More information

On Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator

On Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator On Assessing the Impact of Transportation Policies on Fuel Consumption and Greenhouse Gas Emissions Using a Household Vehicle Fleet Simulator Rajesh Paleti The University of Texas at Austin Dept of Civil,

More information

ON-ROAD FUEL ECONOMY OF VEHICLES

ON-ROAD FUEL ECONOMY OF VEHICLES SWT-2017-5 MARCH 2017 ON-ROAD FUEL ECONOMY OF VEHICLES IN THE UNITED STATES: 1923-2015 MICHAEL SIVAK BRANDON SCHOETTLE SUSTAINABLE WORLDWIDE TRANSPORTATION ON-ROAD FUEL ECONOMY OF VEHICLES IN THE UNITED

More information

Flexible Ramping Product Technical Workshop

Flexible Ramping Product Technical Workshop Flexible Ramping Product Technical Workshop September 18, 2012 Lin Xu, Ph.D. Senior Market Development Engineer Don Tretheway Senior Market Design and Policy Specialist Agenda Time Topic Presenter 10:00

More information

Residential Survey Phase 2 Results

Residential Survey Phase 2 Results 1 Residential Survey Phase 2 Results Prepared for: United Energy December 2017 Contacts: Karyn Wong: kwong@woolcott.com.au Liz Sparham: lsparham@woolcott.com.au United Energy Residential Survey Results

More information

Facts and Figures. October 2006 List Release Special Edition BWC National Benefits and Related Facts October, 2006 (Previous Versions Obsolete)

Facts and Figures. October 2006 List Release Special Edition BWC National Benefits and Related Facts October, 2006 (Previous Versions Obsolete) Facts and Figures Date October 2006 List Release Special Edition BWC National Benefits and Related Facts October, 2006 (Previous Versions Obsolete) Best Workplaces for Commuters - Environmental and Energy

More information

Lecture 2. Review of Linear Regression I Statistics Statistical Methods II. Presented January 9, 2018

Lecture 2. Review of Linear Regression I Statistics Statistical Methods II. Presented January 9, 2018 Review of Linear Regression I Statistics 211 - Statistical Methods II Presented January 9, 2018 Estimation of The OLS under normality the OLS Dan Gillen Department of Statistics University of California,

More information

What do autonomous vehicles mean to traffic congestion and crash? Network traffic flow modeling and simulation for autonomous vehicles

What do autonomous vehicles mean to traffic congestion and crash? Network traffic flow modeling and simulation for autonomous vehicles What do autonomous vehicles mean to traffic congestion and crash? Network traffic flow modeling and simulation for autonomous vehicles FINAL RESEARCH REPORT Sean Qian (PI), Shuguan Yang (RA) Contract No.

More information

Online appendix for "Fuel Economy and Safety: The Influences of Vehicle Class and Driver Behavior" Mark Jacobsen

Online appendix for Fuel Economy and Safety: The Influences of Vehicle Class and Driver Behavior Mark Jacobsen Online appendix for "Fuel Economy and Safety: The Influences of Vehicle Class and Driver Behavior" Mark Jacobsen A. Negative Binomial Specification Begin by stacking the model in (7) and (8) to write the

More information

Cost-Efficiency by Arash Method in DEA

Cost-Efficiency by Arash Method in DEA Applied Mathematical Sciences, Vol. 6, 2012, no. 104, 5179-5184 Cost-Efficiency by Arash Method in DEA Dariush Khezrimotlagh*, Zahra Mohsenpour and Shaharuddin Salleh Department of Mathematics, Faculty

More information

Only video reveals the hidden dangers of speeding.

Only video reveals the hidden dangers of speeding. Only video reveals the hidden dangers of speeding. SNAPSHOT FOR TRUCKING April 2018 SmartDrive Smart IQ Beat Snapshots provide in-depth analysis and metrics of top fleet performance trends based on the

More information

CTR Employer Survey Report

CTR Employer Survey Report CTR Employer Report Employer Id : E12146 Employer : WA State Dept. of Enterprise Services Worksite : Street : 7511 New Market St 7511 New Market St Sw Thank you for completing your Commute Trip Reduction

More information

A REPORT ON THE STATISTICAL CHARACTERISTICS of the Highlands Ability Battery CD

A REPORT ON THE STATISTICAL CHARACTERISTICS of the Highlands Ability Battery CD A REPORT ON THE STATISTICAL CHARACTERISTICS of the Highlands Ability Battery CD Prepared by F. Jay Breyer Jonathan Katz Michael Duran November 21, 2002 TABLE OF CONTENTS Introduction... 1 Data Determination

More information

Professor Dr. Gholamreza Nakhaeizadeh. Professor Dr. Gholamreza Nakhaeizadeh

Professor Dr. Gholamreza Nakhaeizadeh. Professor Dr. Gholamreza Nakhaeizadeh Statistic Methods in in Data Mining Business Understanding Data Understanding Data Preparation Deployment Modelling Evaluation Data Mining Process (Part 2) 2) Professor Dr. Gholamreza Nakhaeizadeh Professor

More information

CTR Employer Survey Report

CTR Employer Survey Report CTR Employer Report Employer Id : E12138 Employer : WA State Dept. of Enterprise Services Worksite : Street : 616 Cherry St 616 Cherry St Se Thank you for completing your Commute Trip Reduction survey.

More information

2018 Linking Study: Predicting Performance on the NSCAS Summative ELA and Mathematics Assessments based on MAP Growth Scores

2018 Linking Study: Predicting Performance on the NSCAS Summative ELA and Mathematics Assessments based on MAP Growth Scores 2018 Linking Study: Predicting Performance on the NSCAS Summative ELA and Mathematics Assessments based on MAP Growth Scores November 2018 Revised December 19, 2018 NWEA Psychometric Solutions 2018 NWEA.

More information

Oregon DOT Slow-Speed Weigh-in-Motion (SWIM) Project: Analysis of Initial Weight Data

Oregon DOT Slow-Speed Weigh-in-Motion (SWIM) Project: Analysis of Initial Weight Data Portland State University PDXScholar Center for Urban Studies Publications and Reports Center for Urban Studies 7-1997 Oregon DOT Slow-Speed Weigh-in-Motion (SWIM) Project: Analysis of Initial Weight Data

More information

The PEV Market and Infrastructure Needs

The PEV Market and Infrastructure Needs The PEV Market and Infrastructure Needs Dahlia Garas, Program Director PH&EV Research Center Presenting Research by: Dr. Gil Tal Dr. Mike Nicholas ITS-DAVIS BOARD OF ADVISORS CLEAN TRANSPORTATION RESEARCH

More information

Do U.S. Households Favor High Fuel Economy Vehicles When Gasoline Prices Increase? A Discrete Choice Analysis

Do U.S. Households Favor High Fuel Economy Vehicles When Gasoline Prices Increase? A Discrete Choice Analysis Do U.S. Households Favor High Fuel Economy Vehicles When Gasoline Prices Increase? A Discrete Choice Analysis Valerie J. Karplus MIT Joint Program on the Science and Policy of Global Change Using National

More information

Disruptive Technology and Mobility Change

Disruptive Technology and Mobility Change Disruptive Technology and Mobility Change What it Might Mean for Urban Transportation Ed Regan Senior Vice President June 1, 2018 Presented at Transport Chicago Ed Regan, SVP, CDM Smith 43-year veteran

More information

Outline. Research Questions. Electric Scooters in Viet Nam and India: Factors Influencing (lack of) Adoption and Environmental Implications 11/4/2009

Outline. Research Questions. Electric Scooters in Viet Nam and India: Factors Influencing (lack of) Adoption and Environmental Implications 11/4/2009 Electric Scooters in Viet Nam and India: Factors Influencing (lack of) Adoption and Environmental Implications Christopher Cherry Assistant Professor-Civil and Environmental Engineering Luke Jones PhD

More information

Predicted response of Prague residents to regulation measures

Predicted response of Prague residents to regulation measures Predicted response of Prague residents to regulation measures Markéta Braun Kohlová, Vojtěch Máca Charles University, Environment Centre marketa.braun.kohlova@czp.cuni.cz; vojtech.maca@czp.cuni.cz June

More information

Linking the Virginia SOL Assessments to NWEA MAP Growth Tests *

Linking the Virginia SOL Assessments to NWEA MAP Growth Tests * Linking the Virginia SOL Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. March 2016 Introduction Northwest Evaluation Association (NWEA

More information

How much oil are electric vehicles displacing?

How much oil are electric vehicles displacing? How much oil are electric vehicles displacing? Aleksandra Rybczynska March 07, 2017 Executive summary EV s influence on global gasoline and diesel consumption is small but increasing quickly. This short

More information

Technological Change, Vehicle Characteristics, and the Opportunity Costs of Fuel Economy Standards

Technological Change, Vehicle Characteristics, and the Opportunity Costs of Fuel Economy Standards Technological Change, Vehicle Characteristics, and the Opportunity Costs of Fuel Economy Standards Thomas Klier (Federal Reserve Bank of Chicago) Joshua Linn (Resources for the Future) May 2013 Preliminary

More information

Vanpooling and Transit Agencies. Module 3: Benefits to Incorporating Vanpools. into a Transit Agency s Services

Vanpooling and Transit Agencies. Module 3: Benefits to Incorporating Vanpools. into a Transit Agency s Services Vanpooling and Transit Agencies Module 3: Benefits to Incorporating Vanpools into a Transit Agency s Services A common theme we heard among the reasons why the transit agencies described in Module 2 began

More information

A Joint Tour-Based Model of Vehicle Type Choice, Tour Length, Passenger Accompaniment, and Tour Type

A Joint Tour-Based Model of Vehicle Type Choice, Tour Length, Passenger Accompaniment, and Tour Type A Joint -Based Model of Vehicle Type Choice, Length, Passenger Accompaniment, and Type Karthik Konduri 1, Rajesh Paleti 2, Ram M. Pendyala 1, and Chandra R. Bhat 2 1 School of Sustainable Engineering and

More information

Public Opinion of Waterloo Region Rapid Transit Proposal May 2011

Public Opinion of Waterloo Region Rapid Transit Proposal May 2011 Public Opinion of Region Rapid Transit Proposal May 2011 Methodology From May 23 to May 25, 2011, Angus Reid Public Opinion conducted an online survey among a residents of Region on behalf of Machteld

More information

Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear. Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method

Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear. Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method Econometrics for Health Policy, Health Economics, and Outcomes Research Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method

More information

Grid Services From Plug-In Hybrid Electric Vehicles: A Key To Economic Viability?

Grid Services From Plug-In Hybrid Electric Vehicles: A Key To Economic Viability? Grid Services From Plug-In Hybrid Electric Vehicles: A Key To Economic Viability? Paul Denholm (National Renewable Energy Laboratory; Golden, Colorado, USA); paul_denholm@nrel.gov; Steven E. Letendre (Green

More information

Powertrain Acceptance & Consumer Engagement Study

Powertrain Acceptance & Consumer Engagement Study Powertrain Acceptance & Consumer Engagement Study July 2009 Chrysler Powertrain Research March 2008 1 Agenda The Need for Powertrain Research Study Overview Highlights of Findings Optimal Powertrain Configurator

More information

Urban & Regional Policy

Urban & Regional Policy Urban & Regional Policy 2015-05-07 Who is the agent? Politicians Local, regional, national Civil servants Consultants Private citizens Citizen organizations Labor organizations Private firms Industrial

More information

New Vehicle Feebates: Theory and Evidence

New Vehicle Feebates: Theory and Evidence New Vehicle Feebates: Theory and Evidence Brandon Schaufele (w/ Nic Rivers) Department of Economics University of Ottawa brandon.schaufele@uottawa.ca Heartland Environmental & Resource Economics Workshop

More information

The Hybrid and Electric Vehicles Manufacturing

The Hybrid and Electric Vehicles Manufacturing Photo courtesy Toyota Motor Sales USA Inc. According to Toyota, as of March 2013, the company had sold more than 5 million hybrid vehicles worldwide. Two million of these units were sold in the US. What

More information

Estimating the impact of monetary incentives on PEV buyers Alan Jenn Scott Hardman Gil Tal. STEPS Fall 2017 Symposium

Estimating the impact of monetary incentives on PEV buyers Alan Jenn Scott Hardman Gil Tal. STEPS Fall 2017 Symposium Estimating the impact of monetary incentives on PEV buyers Alan Jenn Scott Hardman Gil Tal STEPS Fall 2017 Symposium Goal: A better understanding of incentive impacts We employ a stated preference (survey

More information

Who has trouble reporting prior day events?

Who has trouble reporting prior day events? Vol. 10, Issue 1, 2017 Who has trouble reporting prior day events? Tim Triplett 1, Rob Santos 2, Brian Tefft 3 Survey Practice 10.29115/SP-2017-0003 Jan 01, 2017 Tags: missing data, recall data, measurement

More information

Treasure Island Mobility Management Program

Treasure Island Mobility Management Program Treasure Island Mobility Management Program Preliminary Toll Policy Recommendations For Buildout Year (2030) Draft TIDA CAB June 2, 2015 About the Treasure Island Mobility Management Program 2003 2008

More information

Can Vehicle-to-Grid (V2G) Revenues Improve Market for Electric Vehicles?

Can Vehicle-to-Grid (V2G) Revenues Improve Market for Electric Vehicles? Can Vehicle-to-Grid (V2G) Revenues Improve Market for Electric Vehicles? Michael K. Hidrue George R. Parsons Willett Kempton Meryl P. Gardner July 7, 2011 International Energy Workshop Stanford University

More information

HALTON REGION SUB-MODEL

HALTON REGION SUB-MODEL WORKING DRAFT GTA P.M. PEAK MODEL Version 2.0 And HALTON REGION SUB-MODEL Documentation & Users' Guide Prepared by Peter Dalton July 2001 Contents 1.0 P.M. Peak Period Model for the GTA... 4 Table 1 -

More information

Safer or Cheaper? Household Safety Concerns, Vehicle Choices, and the Costs of Fuel Economy Standards

Safer or Cheaper? Household Safety Concerns, Vehicle Choices, and the Costs of Fuel Economy Standards Safer or Cheaper? Household Safety Concerns, Vehicle Choices, and the Costs of Fuel Economy Standards Yoon-Young Choi, PhD candidate at University of Connecticut, yoon-young.choi@uconn.edu Yizao Liu, Assistant

More information

Used Vehicle Supply: Future Outlook and the Impact on Used Vehicle Prices

Used Vehicle Supply: Future Outlook and the Impact on Used Vehicle Prices Used Vehicle Supply: Future Outlook and the Impact on Used Vehicle Prices AT A GLANCE When to expect an increase in used supply Recent trends in new vehicle sales Changes in used supply by vehicle segment

More information

Linking the Georgia Milestones Assessments to NWEA MAP Growth Tests *

Linking the Georgia Milestones Assessments to NWEA MAP Growth Tests * Linking the Georgia Milestones Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. February 2016 Introduction Northwest Evaluation Association

More information

Washington State Voter + Small Business Owner Survey

Washington State Voter + Small Business Owner Survey Washington State Voter + Small Business Owner Survey Summary Report December, 1 1 Overview GMA Research of Bellevue, Washington, conducted a random telephone survey of 2 registered voters and 2 small business

More information

Linking the Alaska AMP Assessments to NWEA MAP Tests

Linking the Alaska AMP Assessments to NWEA MAP Tests Linking the Alaska AMP Assessments to NWEA MAP Tests February 2016 Introduction Northwest Evaluation Association (NWEA ) is committed to providing partners with useful tools to help make inferences from

More information

Linking the North Carolina EOG Assessments to NWEA MAP Growth Tests *

Linking the North Carolina EOG Assessments to NWEA MAP Growth Tests * Linking the North Carolina EOG Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. March 2016 Introduction Northwest Evaluation Association

More information

FutureMetrics LLC. 8 Airport Road Bethel, ME 04217, USA. Cheap Natural Gas will be Good for the Wood-to-Energy Sector!

FutureMetrics LLC. 8 Airport Road Bethel, ME 04217, USA. Cheap Natural Gas will be Good for the Wood-to-Energy Sector! FutureMetrics LLC 8 Airport Road Bethel, ME 04217, USA Cheap Natural Gas will be Good for the Wood-to-Energy Sector! January 13, 2013 By Dr. William Strauss, FutureMetrics It is not uncommon to hear that

More information

School Transportation Assessment

School Transportation Assessment Grade: K-12 Version 1 April 2015 School Transportation Assessment SCHOOL BUS Evaluate the carbon emissions from daily transportation related to your school and identify strategies for more sustainable

More information

Customer Survey. Motives and Acceptance of Biodiesel among German Consumers

Customer Survey. Motives and Acceptance of Biodiesel among German Consumers Customer Survey Motives and Acceptance of Biodiesel among German Consumers A Survey in the Framework of Carbon Labelling Project EIE/06/015/SI2.442654 by Q1 Tankstellenvertrieb GmbH & Co. KG Rheinstrasse

More information

The Dynamics of Plug-in Electric Vehicles in the Secondary Market

The Dynamics of Plug-in Electric Vehicles in the Secondary Market The Dynamics of Plug-in Electric Vehicles in the Secondary Market Dr. Gil Tal gtal@ucdavis.edu Dr. Tom Turrentine Dr. Mike Nicholas Sponsored by the California Air Resources Board Population and Sampling

More information

Linking the Kansas KAP Assessments to NWEA MAP Growth Tests *

Linking the Kansas KAP Assessments to NWEA MAP Growth Tests * Linking the Kansas KAP Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. February 2016 Introduction Northwest Evaluation Association (NWEA

More information

Using Statistics To Make Inferences 6. Wilcoxon Matched Pairs Signed Ranks Test. Wilcoxon Rank Sum Test/ Mann-Whitney Test

Using Statistics To Make Inferences 6. Wilcoxon Matched Pairs Signed Ranks Test. Wilcoxon Rank Sum Test/ Mann-Whitney Test Using Statistics To Make Inferences 6 Summary Non-parametric tests Wilcoxon Signed Ranks Test Wilcoxon Matched Pairs Signed Ranks Test Wilcoxon Rank Sum Test/ Mann-Whitney Test Goals Perform and interpret

More information

Automated Driving - Object Perception at 120 KPH Chris Mansley

Automated Driving - Object Perception at 120 KPH Chris Mansley IROS 2014: Robots in Clutter Workshop Automated Driving - Object Perception at 120 KPH Chris Mansley 1 Road safety influence of driver assistance 100% Installation rates / road fatalities in Germany 80%

More information

Application of claw-back

Application of claw-back Application of claw-back A report for Vector Dr. Tom Hird Daniel Young June 2012 Table of Contents 1. Introduction 1 2. How to determine the claw-back amount 2 2.1. Allowance for lower amount of claw-back

More information

2016 Car Tech Impact Study. January 2016

2016 Car Tech Impact Study. January 2016 2016 Car Tech Impact Study January 2016 Objectives & Methodology Objectives Identify vehicle technologies that are currently being used and that are must haves for future vehicle purchases Determine how

More information

Power and Fuel Economy Tradeoffs, and Implications for Benefits and Costs of Vehicle Greenhouse Gas Regulations

Power and Fuel Economy Tradeoffs, and Implications for Benefits and Costs of Vehicle Greenhouse Gas Regulations Power and Fuel Economy Tradeoffs, and Implications for Benefits and Costs of Vehicle Greenhouse Gas Regulations Gloria Helfand Andrew Moskalik Kevin Newman Jeff Alson US Environmental Protection Agency

More information

May 1, SUBJECT: Demand Forecasting and the Transportation Sector

May 1, SUBJECT: Demand Forecasting and the Transportation Sector James Yost Chair Idaho W. Bill Booth Idaho Guy Norman Washington Tom Karier Washington Jennifer Anders Vice Chair Montana Tim Baker Montana Ted Ferrioli Oregon Richard Devlin Oregon May 1, 2018 MEMORANDUM

More information

Linking the New York State NYSTP Assessments to NWEA MAP Growth Tests *

Linking the New York State NYSTP Assessments to NWEA MAP Growth Tests * Linking the New York State NYSTP Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. March 2016 Introduction Northwest Evaluation Association

More information

Light Duty Vehicle Electrification Discussion on Trip, Vehicle, and Consumer Characteristics

Light Duty Vehicle Electrification Discussion on Trip, Vehicle, and Consumer Characteristics Light Duty Vehicle Electrification Discussion on Trip, Vehicle, and Consumer Characteristics Sven A. Beiker PEEC Fellow and CARS Executive Director, Stanford University Jamie Davies - Consumer Research

More information

Plug-in Hybrid Vehicles Exhaust emissions and user barriers for a Plug-in Toyota Prius

Plug-in Hybrid Vehicles Exhaust emissions and user barriers for a Plug-in Toyota Prius Summary: Plug-in Hybrid Vehicles Exhaust emissions and user barriers for a Plug-in Toyota Prius TØI Report 1226/2012 Author(s): Rolf Hagman, Terje Assum Oslo 2012, 40 pages English language Plug-in Hybrid

More information

Carpooling and Carsharing in Switzerland: Stated Choice Experiments

Carpooling and Carsharing in Switzerland: Stated Choice Experiments Carpooling and Carsharing in Switzerland: Stated Choice Experiments F Ciari May 2012 Project ASTRA 2008/017 - Participants Franz Mühlethaler Prof. Kay Axhausen Francesco Ciari Monica Tschannen Goals Estimation

More information

The Norwegian EV success story: Reaching mass market

The Norwegian EV success story: Reaching mass market The Norwegian EV success story: Reaching mass market Ståle Frydenlund, Senior Adviser / EV Specialist Norwegian EV Association Christina Bu, Secretary General The Norwegian EV Association The Norwegian

More information

HAS MOTORIZATION IN THE U.S. PEAKED? PART 2: USE OF LIGHT-DUTY VEHICLES

HAS MOTORIZATION IN THE U.S. PEAKED? PART 2: USE OF LIGHT-DUTY VEHICLES UMTRI-2013-20 JULY 2013 HAS MOTORIZATION IN THE U.S. PEAKED? PART 2: USE OF LIGHT-DUTY VEHICLES MICHAEL SIVAK HAS MOTORIZATION IN THE U.S. PEAKED? PART 2: USE OF LIGHT-DUTY VEHICLES Michael Sivak The University

More information

HOW REAL PEOPLE VIEW THE FUTURE OF MOBILITY

HOW REAL PEOPLE VIEW THE FUTURE OF MOBILITY HOW REAL PEOPLE VIEW THE FUTURE OF MOBILITY OVERVIEW 1 2 3 Key Points Methodology: Adults overwhelmingly regard January the automotive 20 21, 2018. The industry as innovative, dynamic and changing for

More information

Rui Wang Assistant Professor, UCLA School of Public Affairs. IACP 2010, Shanghai June 20, 2010

Rui Wang Assistant Professor, UCLA School of Public Affairs. IACP 2010, Shanghai June 20, 2010 Rui Wang Assistant Professor, UCLA School of Public Affairs IACP 2010, Shanghai June 20, 2010 A new mode became popular in last few years Massive auto acquisition by urban households Gas price surge Plate

More information