Propensity towards Ownership and Use of Automated Vehicles: Who Are the Adopters? Who Are the Non-adopters? Who Is Hesitant?

TL;DR

This study identifies distinct groups of potential AV adopters and non-adopters using clustering analysis of survey data, providing insights for targeted marketing and policy strategies to facilitate AV adoption.

Contribution

It applies clustering techniques to survey data to categorize potential AV users and non-users, revealing nuanced segments for strategic planning.

Findings

Seven distinct clusters of AV adopters and non-adopters identified.

Adopters are tech-savvy and familiar with AVs, non-adopters include car enthusiasts.

Insights enable targeted marketing and policy development.

Abstract

The objective of this study is to investigate automated vehicle (AV) adoption perceptions, including ownership intentions and the willingness to use self-driving mobility services. In this paper, we use data from the 2018 California Transportation Survey, and use K-means, a clustering technique in data mining, to reveal patterns of potential AV owners (and non-owners) as well as AV users (and non-users) of self-driving services. The results reveal seven clusters, namely Multitaskers/ environmentalists/ impaired drivers, Tech mavens/ travelers, Life in transition, Captive car-users, Public/ active transport users, Sub-urban Dwellers, and Car enthusiasts. The first two clusters include adopters who are largely familiar with AVs, are tech savvy, and who make good use of time during their commute. The last cluster comprise of non-adopters who are car enthusiasts. On the other hand, people who are Life in transition, Captive car-users, Public/ active transport users, and Sub-urban dwellers show uncertain perceptions towards being AV adopters. They are either pursuing higher education, having a busy schedule, supporting for sustainable society via government policies, or have a stable life, respectively. Insights from this study help practitioners to build business models and strategic planning, addressing potential market segments of individuals that are willing to own an AV vs. those that are more inclined to use self-driving mobility services. The "gray" segments identify a latent untapped demand and a potential target for marketing, campaigns, and sales.