Measuring Future Vehicle Preferences: a Stated Preference Survey Approach with Dynamic Attributes and Multi-year Time Frame

The culmination of new vehicle technology, greater competition in energy markets, and government policies to reduce pollution and energy consumption will result in changes to the personal vehicle marketplace. To measure future vehicle preferences, stated preference surveys have been the dominant approach. Prior research has been limited to a narrow focus of accelerating respondents to a hypothetical next vehicle purchasing decision without mimicking the marketplace’s influence on this decision. To explore marketplace influences, this project proposes to use a novel stated preference survey design to analyze vehicle purchasing behavior in a dynamically changing marketplace through the use of dynamic attributes and a six-year hypothetical time window. The survey is divided into three parts: household characteristics, current vehicles, and stated preference. The stated preference section presents respondents with various hypothetical scenarios annually over a future six-year period using one of three experiments. The experiments correspond to changing vehicle technology, fueling options, and taxation policy. Between scenarios, the vehicle, fuel, and policy attributes dynamically change to mimic marketplace conditions. A pilot web-based survey was performed during fall 2010. Mixed logit models showed that respondents responded in behaviorally realistically ways and that the survey design allowed for estimation of important parameters in vehicle choice. Respondents were able to depreciate their vehicles over the five-year hypothetical period and place tradeoffs on the features of vehicles and fuel types. The insights from the survey are also used to suggest refinements to the survey methods and areas for further research. Maness, M. and C. Cirillo 3 INTRODUCTION Predicting consumer preferences for future vehicles is important for industry and governments. Automobile companies and energy producers need to know how much and what kinds of products to sell in the marketplace in order to make a profit. Transportation planners need to know the vehicle characteristics of roadway users in order to create valid car ownership models to predict energy consumption and emissions. Government officials need to know what policies can encourage vehicle ownership that promotes a better environment, improves public health, reduces energy dependence, and promotes economic growth. Stated preference (SP) survey approaches have been the predominant method to determine the demand for new motor vehicles. An early 1990s study used SP surveys to analyze clean-fuel vehicle adoption in California (1). In this study, respondents were presented with hypothetical scenarios with vehicles of varying attributes and asked them to choose their most preferred option. Since then, stated preference surveys have been used to analyze different types of new vehicles as well as various aspects of purchasing behavior. Studies have analyzed demand for battery electric, hybrid electric, plug-in hybrid electric, alternative fuel, and hydrogen fuel vehicles (1-14). Additionally, studies have concentrated on the effects of prior vehicle ownership (2), attitudes (3,4), the purchases of others (5,6), brand recognition (7), and survey framing effects (8,9). Prior vehicle preference SP surveys have been limited in their concentration on a future vehicle purchasing decision without consideration of the marketplace conditions behind the decision. Driving households are at a crossroads. Various vehicle technologies have or will emerge in the market over the next five to ten years. Rising global oil demand is driving up energy prices and creating a competitive marketplace for alternative energy sources. Additionally, local and national governments are interested in using public policy to reduce dependence on oil, decrease air pollution, and combat climate change. These three conditions create an opportunity for changes in the automotive marketplace over the short to medium term. The purpose of this study is to investigate future vehicle preferences over a dynamically changing landscape. To do this, the following tasks were proposed:  Design a stated preference survey with dynamically changing vehicle technology and pricing, varying fueling options, and evolving taxation policy  Administer a web-based survey pilot to determine if the survey design can collect data which allows for estimation of advanced discrete choice models with significant and plausible results  Suggest enhancements to the survey instrument for a larger scale survey This study makes contributions in the survey methods field through the use of a purchasing time window and dynamically changing attributes. Respondents were given scenarios over a six year time window and asked if they would make various purchases. Prior surveys typically looked at either a set time (6,12) or the next vehicle purchase (2-5,7-13). Those approaches isolated the vehicle purchase time from the actual environment. In this study, the survey design allowed the respondent to see the state of the hypothetical environment which allowed for modification of purchasing behavior as needed. This design also allowed for analysis of respondents’ depreciation of their current vehicle. Dynamically changing attributes were used in the survey design to help mimic a real marketplace. The vehicle, fuel, and policy attributes change annually. For example, battery electric vehicle prices fell over a three years period and gasoline vehicle fuel economy increased Maness, M. and C. Cirillo 4 annually. This type of survey design allows for analysis of possible “tipping points” in technological and price changes which may influence new vehicle adoption. DEFINITIONS The following is a brief description of acronyms used in this paper:  BEV – battery electric vehicle, a vehicle that stores electricity in batteries as its only energy source  HEV – hybrid electric vehicle, a vehicle which runs on gasoline but uses larger batteries to aid in the vehicle propulsion  PHEV – plug-in hybrid electric vehicle, a vehicle which stores electricity from the power grid in batteries and includes a gasoline engine. This vehicle can run on battery power alone for short distances and then can switch to gasoline only operation when batteries are depleted.  AFV – alternative fuel vehicle, a vehicle with an internal combustion engine that runs on a liquid fuel that is not gasoline or diesel (e.g. ethanol)  VMT – vehicle miles traveled, a measure of the distance a vehicle travels  MPGe – miles per gallon gasoline equivalent, a measure of the average distance traveled per unit of energy in one US gallon of gasoline PREVIOUS RESEARCH The transportation community has generally approached the task of predicting new vehicle preference via stated preference methods. Bunch et al. (1) performed a three phase survey in the early 1990s to analyze alternative fuel (AFV), flex-fuel, and battery electric vehicle (BEV) adoption in California. Phase two of the survey was a vehicle choice SP experiment where respondents were asked to choose among three different types of vehicle for a future vehicle purchase. The vehicles varied in terms of fuel type, fuel availability, refueling range, price, fuel cost, pollution, and performance. Kurani et al. (2) performed a stated preference survey with reflexive designs in the mid 1990s in California. In this experiment, it was hypothesized that certain multiple-vehicle households had a greater propensity towards BEVs (“hybrid household hypothesis”). The research found that the range limit on BEVs was not a binding travel constraint in many multiple-vehicle households and that the convenience of home refueling was an attractive quality of BEVs. The study estimated that 35 to 40 percent of California households could be “hybrid households.” Ewing and Sarigöllü (3) used SP methods and attitude analysis to study consumer preferences for BEVs and AFVs. This study found that regulation alone was insufficient in creating demand for BEVs in Canada and that technological advances were essential. The research also found that price subsidies were effective and that tax credits would likely be effective as well. Ahn et al (10) looked at alternative fuel vehicles (diesel, natural gas, liquefied petroleum gas) and hybrid electric vehicles (HEVs) to estimate new vehicle purchases and annual usage. Bolduc et al. (4) used SP methods with psychometric data to analyze vehicle preferences in Canada. Hybrid choice models found that environmental concern and appreciation of new vehicle features had significant influence on vehicle choice. Mau et al. (5) looked at vehicle preferences for HEVs and hydrogen fuel cell vehicles using SP methods and a technology vintage model. The analysis confirmed their hypothesis that market share of new technology (“neighbor effect”) affects personal vehicle preferences. Axsen Maness, M. and C. Cirillo 5 et al. (11) surveyed households in Canada and California to compare revealed preference (RP) only methods with SP-RP methods in determining hybrid vehicles preferences. This study found that statistically, RP-only and RP-dominant models performed better, but that SP-dominant models provided better estimates for policy simulations and that willingness-to-pay estimates were more realistic. Musti and Kockelman (12) used a SP survey to calibrate a simulation-based model of household vehicle evolution. This survey presented respondents with twelve different vehicles options and asked for their preferred vehicle under current conditions, under higher fuel price conditions, and with environmental impact information. Eggers and Eggers (9) conducted a web-based SP survey in Germany concentrated on compact and subcompact vehicles for city driving. Their choice set included a gasoline vehicle and three alternative drive train vehicles (combinations of HEV, BEV, and PHEV). The study also tailored the scenarios to respondents’ brand and vehicle class preferences. Beck et al. (8,9) used a web-based SP survey to study the effect of annual and usagebased emissions fees on vehicle ownership. The survey’s alternative set included a new gasoline, diesel, and hybrid vehicles. Respondents’ current vehicle was presented next to the available vehicles to purchase but was not included as a possible alternative in order to reduce hypothetical bias. Hess et al. (13) analyzed results from the California Vehicle Study which asked respondents about the vehicle they likely planned to purchase next. Using this vehicle as an alternative as well as three other vehicles of varying sizes, fuel type, and drivetrain technology, respondents chose their preferred vehicle. Additional approaches to studying future vehicle preferences have included exercises to design new vehicle (design games) (14) and applying information cascade experiments to vehicle preference studies (6). SURVEY DESIGN To analyze consumer preferences for future vehicles, a stated preference approach was adopted. A web-based survey was chosen primarily for its cost and administration time advantages. Table 1 summarizes the characteristics and methodology of the survey. The survey consisted of three sections: Household Characteristics, Current Vehicle, and Stated Preference. The Household Characteristics section gathered information about the respondents and their households. The Current Vehicle section asked respondents to describe various characteristics about their current vehicle, such as make and model, fuel economy, and vehicle price. The Stated Preference portion of the survey involved presenting respondents with one of three stated choice experiments: Vehicle Technology, Fuel Type, and Taxation Policy. Each respondent randomly received one SP experiment. The Vehicle Technology experiment had a 50% chance of being displayed while the other two experiments each had a 25% chance. Each stated choice experiment generated multiple SP observations over a six year time period, from 2010 to 2015. The variables in the scenarios changed from year to year when plausible. For example, vehicle price generally increased over time, hybrid vehicle tax credit decreased with time, and the range for gasoline vehicles remained constant. Two scenarios per year were presented for a total of 12 observations. Respondents were given the following instructions for this section:  Make realistic decisions. Act as if you were actually buying a vehicle in a real life purchasing situation. Maness, M. and C. Cirillo 6  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. After the instructions, respondents were also given definitions of the vehicle types in the choice set and the attributes in the scenario table. Vehicle Technology Experiment The Vehicle Technology experiment focused on presenting respondents with varying vehicle characteristics and pricing in order to discover preferences for vehicle technology. This experimental design consisted of four alternatives and five variables with a choice set size of eight. Four alternatives – current vehicle and a new gasoline, HEV, and BEV – were shown to respondents. These vehicle platforms were chosen because they appear to have a good chance for market share in the United States over the next five years. Gasoline vehicles are the traditional option, while hybrid electric vehicles have grown in market share in the US. While battery electric vehicles are new to the marketplace, there has been significant interest in exploring this paradigm by major automobile manufacturers. The variables of interest in the vehicle technology experiment included vehicle price, fuel economy, refueling range, emissions, and vehicle size. Vehicle price, presented in U.S. dollars, depended on the size of the vehicle and increased annually. Fuel economy was presented in miles per gallon (MPG) for gasoline and hybrid vehicles. Refueling range was presented as the miles between refueling or recharging. Emissions were displayed as the percent difference in emissions in comparison to the average vehicle in 2010. Electric vehicles were stated to have no direct emissions. Vehicle sizes were based on the US EPA vehicle size system. The choice set for the vehicle technology experiment included all permutations of buying or not buying a new vehicle (gasoline, hybrid, or electric) and selling or retaining the current vehicle. Fuel Type Experiment The Fuel Type experiment presented respondents with different fuel options to infer the effect of fuel characteristics on future vehicle purchases. This experimental design consisted of four alternatives and four variables with a choice set size of seven. Four fuel types were shown to respondents – gasoline, alternative fuel, diesel, and electricity. These fuel types are currently established in Maryland’s marketplace – gasoline, alternative (ethanol), and diesel via fueling stations and electricity via the home. The variables of interest in the fuel type experiment included fuel price, fuel tax, average fuel economy, refueling availability, and charging time. The fuel price and fuel tax were presented in US dollars per gallon or gallon equivalent for electric. The fuel economy was presented as the average expected fuel economy for a vehicle that runs on that fuel type and measured in MPG or MPGe (for BEVs). The refueling availability was presented as the average Maness, M. and C. Cirillo 7 distance to a refueling station from the respondent’s home. Charging time was presented as the time it would take to recharge an electric vehicle from the home. The choice set for this experiment included keeping and selling the respondent’s current vehicle or buying a new gasoline, alternative fuel, diesel, battery electric, or plug-in hybrid electric vehicle. Taxation Policy Experiment The taxation policy experiment presented respondents with different toll and tax policies to infer their effect on future vehicle purchases. For the 2010 and 2011 scenarios, the experimental design consisted of four alternatives and two variables with a choice set size of eight. For the 2012 through 2015 scenarios, the experimental design consisted of four alternatives, three variables, and nine choices. For reasons similar to the Vehicle Technology experiment, four alternatives – current vehicle, new gasoline vehicle, new HEV, and new BEV – were shown to respondents. The variables of interest in the taxation policy experiment included: income tax credits, toll cost, and vehicle-miles traveled (VMT) fee (for scenario years 2012 through 2015). The income tax credit, measured in US dollars, attempted to encourage adoption of new technology through reducing one’s tax burden. Tax credits were shown for HEVs and BEVs based on current US federal guidelines for credits. The toll cost variable was presented to respondents as the percent reduction in normal toll prices for users of that vehicle type. The VMT tax rate was presented as a cost in US dollars per 1000 miles traveled that would be collected by the respondent’s insurance provider. The choice set for the taxation policy experiment included all permutation of buying or not buying a new vehicle (gasoline, HEV, or BEV) and selling or retaining the current vehicle. For the 2012 through 2015 scenarios, an additional choice was added to keep one’s current vehicle and drive less. DESCRIPTIVE STATISTICS A sample was collected using a multi-stage cluster design by county and zipcode with 141 completed surveys. The sample had the following descriptive statistics: