Modeling Users’ Adoption of Shared Autonomous Vehicles Employing Actual Ridership Experiences

Roya Etminani-Ghasrodashti, Ronik Ketankumar Patel, Sharareh Kermanshachi, Jay Michael Rosenberger, Ann Foss

Research output: Chapter in Book/Report/Conference proceedingChapter

29 Scopus citations

Abstract

Despite the growing interest in implementing shared autonomous vehicles (SAVs) as a new mobility mode, there is still a lack of methodologies to unpack SAV adoption by individuals after experiencing self-driving vehicles. This study aimed to fill this gap by analyzing data collected from a users’ survey of a self-driving shuttle piloted downtown and on a university campus in Arlington, TX. Employing structural equation modeling, the hypothesized relationships between SAV adoption and key factors were tested. Data analyses indicated that individuals with limited access to a private vehicles, low-income people, young adults, university students, males, and Asians were more likely to ride this new service. Furthermore, results showed that SAV service attributes, including internal and external service performance and usual transportation mode, affected users’ willingness to continue using the service in the future. The study also highlighted the role of trip waiting time,-purpose, and frequency on SAV adoption. Our model simultaneously considered usual transportation mode and trip frequency as factors that could mediate the role of vehicle ownership on SAV adoption. The results suggested that participants with greater access to a private vehicle were strongly interested in using private vehicles and less likely to use the ridesharing alternative, consequently they less frequently used the piloted SAV. The outcomes from this study are expected to inform planners with advanced knowledge about emerging technology to help them to adjust SAV policies before autonomous vehicle services are fully on the roads.

Original languageEnglish (US)
Title of host publicationTransportation Research Record
PublisherSAGE Publications Ltd
Pages462-478
Number of pages17
Volume2676
Edition11
DOIs
StatePublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Mechanical Engineering

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