Spatial Distributions of Attitudes and Preferences towards Autonomous Vehicles

Prior research on attitudes towards autonomous vehicles (AVs) shows variation across a set of demographic and socio-economic factors, but few consider spatial patterns. We investigate the spatial distributions of attitudes and preferences towards AVs from a U.S. nationally representative on-line panel. We examine 1) four attitudinal dimensions established by an exploratory factor analysis – technology acceptance, driving enjoyment, risk-taking, and traffic regulation – and their spatial variations; and 2) public viewpoints and preferences in purchasing, sharing, and using privately-owned and shared AVs. Findings suggest that the northeastern US is more receptive to AVs, compared to other parts of the country.


research questions and hypotheses
A growing body of scholarship examines public perceptions and attitudes towards AVs in order to understand future AV adoption rates and their potential impact. This prior research shows variation across a set of demographics and socio-economic factors, but does not consider spatial patterns (Bansal, Kockelman, and Singh 2016;Menon et al. 2019;Wang et al. 2020). One exception is Nodjomian and Kockelman (2019) who found that access to jobs, the mix of land uses, and home location choice are associated with levels of interest in AVs and anticipated use of AV's. Our investigation is further motivated by the observation that the heterogeneity in acceptance of AVs exists across regions and countries (Schoettle and Sivak 2014;Haboucha, Ishaq, and Shiftan 2017). Specifically, we hypothesize that attitudes and preferences towards AVs may also be shaped by regional location in the United States. We focus on the following research questions:

methods and data
Using an on-line panel from Qualtrics, a survey was conducted in September 2018 and is demographically representative of the adult US population (see Wang et al. 2020). After removing invalid and incomplete responses for demographic information, we had 782 respondents (from 834 in total). We collected information on attitudes, socio-demographics, location (postal code), and travel behavior (commuting mode, commuting time, daily travel mode, and car ownership). The main limitation of on-line panels is that survey-takers are paid and may be more tech-savvy. We also examined how representative our data is at the regional level. While less so than nationally, we found slightly more survey respondents from the northeast, and fewer from the west, but most demographic variation differed by only a few percent compared to census data for the region. The one exception was more male respondents in the northeast. Our questions and responses on views towards privately-owned and shared AVs are in Table 1.   About 37% of our sample has a positive view of AVs, with one-third likely to purchase their own. Only 24% of our sample would use a self-driving taxi and only 21% would share a self-driving taxi with a stranger. Respondents report marginally more willingness to wait when using ridesourcing services for travel to/from destinations such as restaurants compared to work or shopping.
Additionally, we surveyed attitudes towards new technologies using a 5-point Likert scale from "strongly disagree" to "strongly agree." These questions are shown in Table 2 including the results of a factor analysis that decomposed these into four factors defined as "pro-technology", "driving enjoyment", "risk avoiders", and "regulating traffic". The shaded areas in Table 2 show the largest loadings for each variable.
We used Getis-Ord Gi* (Anselin and Getis 1992), a distance-based statistical method to identify statistically significant clusters of values across a spatial distribution. The method assesses each value within the context of its neighboring values. Using region-scaled (250 to 325 miles) distance bands, we examined spatial distributions of: (1) the four attitudinal factors; (2) preferences for AVs; and, (3) willingness to wait when using ridesourcing services.

findings
The results of our Getis-Ord Gi* analysis for these four factors is shown in Figure 1. A hotspot analysis for preferences towards AVs is shown in Figure 2. In general, "Positive" (or "Yes") responses are spatially clustered around large, dense urban regions such as New York City or the Bay Area in California (home of Silicon Valley), while "Non-positive" (or "Uncertain" / "No") responses are significantly clustered in the South. For the attitudes towards AVs, as shown in Figure 2 (a), positive attitudes are significantly more likely in the Bay Area, Arizona, New England, and the Washington DC metro. Figure 2  Spatial variation in willingness to wait is shown in Figure 3. The results show that the duration respondents are willing to wait for trips to/from grocery stores and restaurants is significantly higher in parts of the South and Midwest. Despite no significant reported interest in sharing AV taxis, respondents in these areas may be more amenable to sharing because of their increased willingness to wait.
While we observe significant regional clustering, demographic factors may covary across regions in a way that explains the spatial variability. We therefore summarize a range of variables by US census-designated region including an ANOVA or Chi-Square test in Table 3 using weighted data to represent the regional population. Weights were calculated for gender, age, income, and region based on a statistical raking algorithm using the "survey" package in R (Lumley 2020). As displayed in Table 3, respondents from the Northeast region do have substantially more positive attitudes toward AVs overall and they are more "pro-technology", but the demographics of the Northeast also deviate from other regions. The Northeast has higher average and median Spatial Distributions of Attitudes and Preferences towards Autonomous Vehicles Transport Findings Figure 3: Spatial hotspot analysis of waiting time for ridesourcing services.

Spatial Distributions of Attitudes and Preferences towards Autonomous Vehicles
Transport Findings automobile accessibility than other regions. When we look at the automobility accessibility of each city, we find that New York City and other large, older cities of the Northeast have greater access to jobs within 45 minutes. Moreover, the Northeast and West regions have, on average, higher traffic congestion levels and road density levels than the South and Midwest regions. Demographically, respondents from the Northeast live in more urbanized locations, are wealthier, and more familiar with AVs overall.

Spatial Distributions of Attitudes and Preferences towards Autonomous Vehicles
Transport Findings