Consumer acceptance and travel behavior : impacts of automated vehicles : final report.
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This study investigates consumer acceptance and potential travel behavior changes associated with automated vehicles (AVs), addressing a critical gap in empirical evidence for transportation policy. While AVs promise societal benefits such as reduced accidents and congestion, their actual impact depends on market adoption rates and usage patterns, which remain highly uncertain. The research aims to determine the likelihood of AV use, the factors influencing acceptance, the appeal of the technology, and potential shifts in vehicle ownership, vehicle miles traveled (VMT), and traffic congestion. The researchers conducted a two-phase study in the Austin metropolitan area in May and June 2015. The first phase involved an online survey of 556 residents, which included a video demonstration of AV technology to ensure informed responses. The second phase consisted of face-to-face interviews with 44 participants to gather qualitative data on potential travel behavior changes. The study utilized the Car Technology Acceptance Model (CTAM) to analyze psychological and behavioral predictors of adoption, rather than relying solely on demographic data. Findings indicate that the sample was evenly split, with 50% intending to use AVs and 50% not. Demographic variables like age and income were poor predictors of intent; instead, psycho-social factors were dominant. Key predictors of high intent to use included having a physical condition prohibiting driving, perceiving AVs as safer, frequent use of smartphones and transportation apps, low concern for data privacy, and believing the technology would be fun and easy to learn. Conversely, the primary barriers to adoption were lack of trust in the technology (41%), safety concerns (24%), and cost (22%). Regarding travel behavior, respondents preferred private ownership over shared services by a 3-to-1 margin. Most participants anticipated no change in the number of vehicles owned, intending to replace rather than reduce their current fleet. Local VMT was expected to remain stable, though inter-city travel frequency was projected to increase due to reduced driver fatigue. The study concludes that consumer psychology, rather than demographics, drives AV acceptance. The potential for AVs to mitigate travel penalties by making trips more productive and relaxing suggests a risk of increased VMT and a shift away from public transit, potentially exacerbating congestion. The findings provide an initial evidence base for policymakers, highlighting the need for public education to address knowledge gaps and trust issues. The research underscores that while AVs offer significant safety and mobility benefits, their net societal impact depends heavily on whether consumers adopt them for personal ownership or shared use, and how they alter existing travel routines.
Key finding
Intent to use self-driving vehicles was evenly split among Austin residents, with psycho-social factors such as technology adoption orientation and data privacy concerns being stronger predictors of acceptance than demographic variables like age or income.
Methodology
mixed_methods
Sample size: 556
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: observational prevalence, self report data