While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.
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The survey asked questions about benefits of and concerns of CAVs, crash history, opinions about speed regulations, willingness to pay (WTP) for and interest in CAV technologies, demographics, travel patterns, among many others. Please see later section about survey designing to know more about the details of the survey instrument.
Please see Tirumalachetty et al. (2009) for micro-simulation of demographics.
The simulation-based approaches to forecast the adoption of any new technology generally allows population to evolve each year (see Bansal and Kockelman (2016) for the simulation-based forecast of CAV adoption in the next 30 years). To illustrate the importance of estimating the peer-pressure effect in the context of CAV adoption, consider the following example: if an individual is estimated to buy CAV technology when at least 50% of his/her relatives own that technology, then in the forecasting simulation if this situation occurs at particular year and individual’s WTP is more than the price of that technology, then individual is allowed to buy that technology.
Kockelman and Li (2016) provided valuation of CAVs’ safety benefits, but did not account for an overall change in VMT.
This study conducted survey through a professional survey firm, but the data for Austin study were collected by distribution unpaid survey among Austin neighborhood association and also at social-networking websites. Though both studies calculated sample weights, but original sample is relatively unbiased for the current study as compared to the Austin study.
Bansal et al. (2016) used ordered probit specification to estimate WTP of Level 3 and Level 4 Automation, but this study uses interval regression for the same.
Respondents’ crash history and opinions about speed law enforcement were asked to explore correlation of such attributes with their opinions of and WTP for CAV technologies.
Respondents who completed the survey in less than 15 min were assumed to have not read questions thoroughly, and their responses were discarded. Respondents were provided with NHTSA’s automation levels’ definitions and, subsequently, were asked whether they understood this description or not. Those who did not understand it (5.7%, or 65 respondents) were considered ineligible for further analysis. Certain other respondents were also considered ineligible for further analysis: those younger than 18 years of age, reporting more workers or children than the household size, reporting the same distance of their home from various places (airport and city center, for example), and providing other combinations of conflicting answers.
The categories of “Master’s degree holder female and 18–24 years old” and “Master’s degree holder male and 18–24 years old” were missing in the sample data. Thus, these population categories were merged with “Bachelor’s degree holder female and 18–24 years old” and “Bachelor’s degree holder male and 18–24 years old,” respectively, to create population correction weights.
There are 32 combinations of traits (4 × 4 × 2 = 32), but there are only 26 categories because some of the categories cannot exist. For example, the number of workers cannot exceed household size. A category “household with more than three members, more than two workers, and no vehicle” was missing and was merged with “household with more than three members, two workers, and no vehicle” in the population.
Respondents were informed that connectivity can be added to an existing vehicle using a smartphone and some additional equipment with dedicated short-range communications (DSRC) technology and inertial sensors. This feature can be used to send alerts to the driver in form of audible sounds (like a message to “slow down” when congestion is forming up ahead or the roadway is deemed slippery) or in text format (like real-time travel times to one's destination). Vehicle to vehicle (V2V) and vehicle to infrastructure (V2I), both can be facilitated by DSRC.
Another interesting opinion summary indicates that most Texans (80%) are not ready to send their children alone in self-driving vehicles and around the same proportion of respondents (78%) are not in support of banning conventional vehicles when 50% of all new vehicles are self-driving.
Before asking questions about the adoption rates of SAVs, respondents were given a definition for SAV and the following pricing information for current ridesharing and carsharing services: “Taxis in most U.S. cities presently cost about $2.50 to $3.50 per mile. UberX and Lyft (companies providing real time on-demand taxi service) charge about $1.50 per mile. Car2Go (a company providing point-to-point carsharing service) charges $0.80 to $1.25 per mile within its Austin-area geofence and $15 per hour of parking outside of this area”.
Prior to asking respondents about their home-location shift decisions, they were provided with the following information: “Autonomous vehicles may make travel easier for many people, and some travelers may decide to live further from the city center, their workplaces, and their children’s schools. Alternatively, households living in urban locations will be able to access a low cost (for example, $1.50 per mile) shared fleet of autonomous vehicles. This will allow them to let go of vehicles they presently own, and turn to other transportation options (like walking, biking, and utilizing autonomous buses for some trips)”.
Around 45% of Texans eat or drink at least once a week while driving, and this proportion is expected to increase to 56% while riding in self-driving vehicles.
Respondents were asked to choose WTP interval (e.g., $1500 to $2999 to add automation) and also provided with options of “$3000 or more” and “$1000 or more” in the questions about WTP to add automation and connectivity, respectively. Thus, the response variable is right-censored interval data. Interval regression is an extension of linear regression and reflects all interval boundaries as known values, unlike an ordered probit or logit model specification.
Interval regression can be used to model point, interval, right-censored, and left-censored data types.
As an exception, single respondents are estimated to have higher WTP to add Level 4 automation (other attributes held constant), but their adoption timing depends more on their friends’ adoption rates.
Since household vehicle ownership is not controlled here, the respondents showing negative inclination towards SAVs may have higher vehicle ownership, on average.
However, even respondents who experienced more moving violations in the past do not attach statistical significance to the SAVs’ utility of saving them from future violations at $3 per mile.
This model alone can obtain inferences about two groups’ characteristics: those “who want to shift closer to the city center or stay at the same location” and those “who want to shift farther from the city center or stay at the same location.” However, to appreciate the characteristics of population groups “who want to shift closer to the city center” and “who want to shift farther from the city center”, a new binary logit model was estimated, so as to explore the individual characteristics of those “who want to stay at the same location” after AVs and SAVs become common modes of transport. For example, according to OP model estimates, those who are familiar with UberX are either likely to shift farther from the city center or stay at the same location, but the binary logit model suggests that these individuals are likely to shift. This new binary logit model clarifies that these individuals are expected to shift farther from the city center.
Safety- and tech-based predictors were not used in these models’ specifications.
However, individuals who travel more, all other attributes remaining equal, are more likely to support tolling-related Policies 2 and 3.
Respondents’ (population-corrected) expectation of an increase in the number of long-distance trips (over 50 miles, one-way) they make each month, after having access to/adopting an AV, is 1.3 (long-distance trips per person, per month), suggesting a 156% increase across the (population-corrected) sample’s total long-distance trip-making. In other words, long-distance trip-making frequencies are predicted to more than double, following access to AVs.
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Bansal, P., Kockelman, K.M. Are we ready to embrace connected and self-driving vehicles? A case study of Texans. Transportation 45, 641–675 (2018). https://doi.org/10.1007/s11116-016-9745-z
- Connected and autonomous vehicles
- Ordered probit
- Interval regression
- Public opinion survey
- Willingness to pay