Clusters of potential autonomous vehicles users according to propensity to use individual versus shared vehicles

Abstract

As the widespread use of autonomous vehicles (AVs) becomes increasingly likely, an important consideration is the extent to which individuals prefer either private ownership or shared use modes. Both modes are currently evolving, each with distinct but overlapping challenges. Understanding the preferences of different population segments can provide insights into where to focus initial efforts to attract individuals into the market, especially in terms of promoting the uptake of shared AVs to optimise the potential positive outcomes of AVs (e.g., crash reduction) while reducing possible negative outcomes (e.g., increased congestion). The results from a sample of 1,345 Australians aged 16+ years (97% of whom were drivers) were analysed using latent profile analysis. Five discrete classes were identified on the basis of their (i) self-reported knowledge of AVs; (ii) perceptions of the positive and negative outcomes of AVs; and (iii) AV usage intentions. The classes were titled Nonadopters (29% of the sample), Ride-sharing (20%), AV ambivalent (19%), Likely adopters (17%), and First movers (14%). The results indicate the types of individuals who may be most likely to be early adopters and the implications for public policies designed to encourage socially optimal forms of adoption.

Shared autonomous vehicle services: A comprehensive review

“…In fact, Bösch et al (2018a) and Pakusch et al (2018) affirm that private cars will continue to exist. Although Pettigrew et al (2019) conclude high interest for SAVs in Australia, 29% of the survey participants are found to be non-adopters of any kind of autonomous vehicle. Based on consumer adoption rules and using a market dissemination model called ECAN (Exclusivity, Choice, Access and Need), Grush & Niles (2018) affirm the dominance of private vehicles for next 30 years.…”

Interrelationships among predictors of automated vehicle acceptance: a structural equation modelling approach

“…Fourth, in order to enhance technology savviness, policy makers, manufacturers and public transport companies should develop strategies to identify the segments who are more amenable to adoption (Pettigrew, Dana, and Norman 2019). Opportunities for trial should be offered to increase the visibility of usage (Pettigrew, Dana, and Norman 2019).…”

Autonomous vehicles and cycling: Policy implications and management issues

“…A primary issue identified by the interviewees was the need to make decisions about the infrastructure requirements of transport systems featuring AVs to encourage optimal use of active transport options. The view of many interviewees that ride-sharing models of AV use have the potential to reduce rates of private car ownership are supported by analyses of consumer responses to traditional (i.e., non-autonomous) ride- and car-sharing services (e.g., Uber) on car ownership levels ( Liao et al, 2020 ; Schmidt, 2020 ; Ward et al, 2019 ) and modeling of the anticipated further effects once ride-sharing services are available in autonomous mode ( Pettigrew et al, 2019c ). Research investigating the benefits of providing cycling infrastructure in the pre-AV environment has found that improved facilities can increase participation rates in a highly cost-effective manner ( Aldred et al, 2018 ; Goodman et al, 2013 , Goodman et al, 2014 ; Keall et al, 2015 ).…”