Cluster Analysis

Cluster analysis is a multivariate technique widely used to identify structures based only on the information found in the data (Anderberg, 1973). Its primary objective is to restructure the data into groups, with a high degree of association within the elements of each group (Tan, 2006). Cluster analysis is used as an exploratory technique to uncover respondent subgroups with seemingly diverse characteristics, to derive insights on the decision-making processes of business entities and/or individuals (Guo et al., 2016). This technique has been employed in transportation literature for the last several decades.

Chang et al. (1992) used cluster analysis and discriminant analysis to determine the impact of commuter driving behavior on the rapid growth in suburban populations. Ng et al. (1998) employed cluster analysis to unearth groups of private and commercial drivers based on how much importance they placed on trip factors that influenced their commute trips. Guo et al. (2016) employed cluster analysis to understand the correlation between truck freight carriers’ operational and behavioral characteristics, and the factors that foster/impede their willingness to collaborate with rail freight carriers. A two-step cluster analysis is preferred over hierarchical or portioning cluster analysis due to its ability to simultaneously handle both categorical and continuous as well as its capacity to be flexible in defining the required number of clusters (Chui et al., 2001). Following these works, a two-step cluster analysis was employed for identifying the various autonomous vehicle consumer market segments. The eleven variables (factors) determined previously through factor analysis were used for conducting the cluster analysis in this study.

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The two-step cluster analysis identified consumer market segments based on the factors influencing the adoption (or non-adoption) of autonomous vehicles. These factors comprise of five potential benefits, and six potential concerns with autonomous vehicles. These benefits include fewer traffic crashes and increased roadway safety, less stressful driving experience, less traffic congestion, more productive use of travel time, and increased fuel efficiency. The potential concerns include safety of the vehicle occupants and other road users, system/equipment failure, performance in unexpected traffic situations, difficulty in determining liability in the event of a crash, privacy risks from data tracking, and loss in human drying skill over time.

Based on the results from the two-step cluster analysis procedure employed using SPSS 23 (IBM Corp., 2014), four different autonomous vehicle consumer market segments are obtained. These market segment centers represent a mathematical average of responses for members within each market segment. In order to understand the intended adoption potential of these market segments, the average scores obtained for intended adoption for said clusters were correlated along with the scores obtained for the perception variables under each cluster. The findings are as shown in Table 4-3.

The first market segment (n=513, 19.3%) identified by the two-step cluster analysis, the benefits-dominated segment, included consumers who foresee benefits with the introduction of autonomous vehicles. Respondents under this market segment believe that the proposed benefits of autonomous vehicles such as fewer traffic crashes and increased roadway safety, less stressful

driving experience, more productive use of travel time, increased fuel efficiency, and less traffic congestion are more likely to occur with the introduction of autonomous vehicles. It is likely that

respondents who belong to this market segment are usually early adopters of new technology. It was also seen that their positive outlook towards potential benefits with autonomous vehicles also

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reflected on their intended adoption of autonomous vehicles as well, evidenced by their high adoption score.

Table 4-3 Segment Means for Each Respondent Group Based on Perception of Benefits and Concerns of Autonomous Vehicles (Bold Numbers Indicate that the Majority of Respondents in this Segment Consider this Factor Likely or Extremely Likely)

Description of Autonomous Vehicles Perception Variables Benefits- Dominated Cluster (N=513) Uncertain Cluster (N=732) Well- Informed Cluster (N=811) Concerns- Dominated Cluster (N=602) Potential Benefit - Fewer traffic crashes

and increased roadway safety 4.65 3.08 4.14 2.47

Potential Benefit - Less stressful driving

experience 4.62 2.89 4.21 2.27

Potential Benefit - Less traffic congestion 4.18 2.46 3.35 4.89 Potential Benefit - More productive (than

driving) use of travel time 4.57 2.97 4.24 2.57

Potential Benefit - Increased fuel

efficiency 4.21 3.07 3.85 2.69

Potential Concern - Safety of the vehicle occupants and other road users such as pedestrians, bicyclists.

2.35 3.43 4.26 4.43

Potential Concern - System/equipment

failure or AV system hacking 2.77 3.48 4.4 4.73

Potential Concern - Performance in (or response to) unexpected traffic situations, poor weather conditions

2.82 3.49 4.44 4.64

Potential Concern - Difficulty in determining who is liable in the event of a crash

2.46 3.15 3.63 4.52

Potential Concern - Privacy risks from data tracking on my travel locations and speed

2.68 3.07 3.67 4.59

Potential Concern - Loss in human driving

skill over time 2.46 3.34 3.45 4.49

Likelihood of adopting autonomous vehicles when they become available in the market

4.24 2.54 3.39 1.74

The second market segment (n=732, 27.5%), the uncertain segment, included consumers who are skeptical about both the potential benefits as well as the potential concerns with autonomous vehicles. It is highly likely that this segment is relatively unexposed towards the discussions and discourse on emerging vehicle technologies such as autonomous vehicles.

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Alternatively, it is also possible that this segment is usually skeptical about emerging technologies and often less likely to feature among the list of early adopters. Their adoption scores tend towards the unlikely range, perhaps unsurprising given their skepticism.

The third market segment (n=811, 30.5%), the well-informed segment, included consumers who are equally aware of the potential benefits and concerns with autonomous vehicles. While consumers in this market segment feel that the proposed benefits such as more productive use of

travel time, less stressful driving experience, and fewer traffic crashes are likely to occur, they are

also equally concerned about issues such as performance of the AV in unexpected traffic situations,

possible system/equipment failure, and other safety-related concerns with the introduction of

autonomous vehicles. Their adoption scores tend towards the likely range most likely indicating a wait-and-watch approach before immersing themselves into the adoption process (see Table 4-3). The final market segment (n=602, 22.6%), the concerns-dominated segment, consisted of consumers who are increasingly concerned about the potential issues with autonomous vehicles. Respondents under this market segment felt they would likely to be more concerned about possible

system/equipment failure, performance in unexpected traffic and weather conditions, privacy risks from data tracking, difficulty in determining liability in the event of a crash, loss in human driving skill over time, and safety of the vehicle occupants and other road users. It seems that these

concerns eventually influence their autonomous vehicle adoption decisions as well, as evidenced by their low mean intended adoption scores as shown in Table 4-3.

The two-step cluster analysis employed in this study reveals interesting insights on autonomous vehicle consumer market segments. Aside from the conventional benefits- and concerns-dominated market segments, the uncertain and the well-informed market segments create value in enhancing our understanding of the consumer demographics in a world with autonomous

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vehicles. This information would likely provide auto-manufacturers, and transportation professionals with market segments that provide different opportunities and challenges during market penetration of such technologies. Insights from this cluster analysis could also be used to devise different approaches to be adopted so as to prepare various consumer segments for a world with autonomous vehicles.