Citation

Wang S, Li Z, Wang Y, Wyatt DA. Transp. Policy 2024; 147: 140-157.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.tranpol.2023.12.022

PMID

unavailable

Abstract

This study is motivated by the need to quantitatively understand the dynamic nature of Automated Vehicle (AV) education's effectiveness, given the fact that existing research has identified factors that associated with the change of AV acceptance between before and after education. To address this research gap, an online state-wide survey study was conducted in Kentucky, USA, offering the opportunity to receive AV education via watching a video and assessing survey respondents' AV acceptance before and after education. By employing a Structural Equation Modeling (SEM) approach with multi-layer endogenous variables included, what makes AV education effective and the underlying reasons were identified and quantitatively revealed with focusing on respondents' socio-demographics, affordability, travel needs, built environment, and exposure level to AV knowledge. As a result, significant factors that impact the probability of "AV acceptance being increased after education" were identified from the aforementioned perspectives. Overall, older generations are less likely to increase their AV acceptance after education for two particular reasons: (a) concerning the comfortableness of Level 5 (full) automation and (b) living in the rural setting, which leads to fewer chances of experiencing automated driving. From a gender perspective, women are more likely to be affected by AV education because they are less familiar with AV technology than men. Also, our results suggest that Level 5 comfort rating plays the most influential role in determining the likelihood of increasing AV acceptance after education. The likelihood is improved by 33% if there is a one-unit increase in the Level 5 comfort rating. As such, the research informs AV policymakers of identities that make AV education effective for future AV education considerations.

Language: en

Keywords

Acceptance; Automated driving; Education; Effectiveness; Policy; Structural equation modeling