Investigating Autonomous Vehicle Impacts on Individual Activity-Travel Behavior
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 the impact of autonomous vehicle (AV) availability on individual activity-travel behavior, addressing a gap in existing literature that often relies on simulations with a priori assumptions about user behavior. The authors argue that previous methods fail to capture individual-level heterogeneity and psycho-social determinants. To address this, the paper employs a direct survey-based modeling approach to examine five dimensions of short-term activity-travel choices: additional local area trips, trip distances for shopping/eating and leisure, additional long-distance road trips, and willingness to accept longer commute times. The research utilizes data from a 2019 survey of 899 respondents in the Austin, Texas metropolitan area. The analytic framework combines socio-demographic variables, built environment attributes, and four latent psycho-social constructs: technology savviness, safety concern, variety-seeking lifestyle, and interest in the productive use of travel time. The methodology involves a two-step estimation process. First, a multivariate linear regression model estimates the latent constructs based on attitudinal indicators. Second, these estimated constructs, along with exogenous variables, are used in a multivariate ordered-response probit model to simultaneously explain the five activity-travel outcomes. This approach allows for the assessment of both direct effects of demographics and indirect effects mediated by psychological factors. The results indicate that AVs are unlikely to substantially increase overall trip-making levels; only about 40% of respondents indicated they would make additional local trips. However, local area trips for shopping, leisure, and commuting are likely to become longer. The most significant impact is projected on long-distance travel, with over 50% of respondents indicating they would make more long-distance road trips. The study also highlights considerable heterogeneity in responses across population segments and geographies. Psycho-social factors, particularly variety-seeking lifestyle and interest in productive travel time, were significant determinants of behavioral changes, underscoring the importance of these latent constructs in predicting AV adoption effects. The significance of this work lies in its demonstration that direct survey-based modeling provides a more accurate reflection of behavioral changes than factor-modification approaches, which often assume uniform responses. The findings suggest that AV campaigns and system designs must account for heterogeneity to ensure equity and avoid leaving certain groups behind. By integrating psycho-social latent constructs, the study offers improved predictive fit and provides insights for proactive policy-making, emphasizing that AV impacts are not uniform but vary significantly based on individual attitudes and lifestyle preferences. This approach supports the development of safer, more equitable, and community-driven AV systems.
Key finding
AVs are projected to significantly increase long-distance road trips and extend local trip distances, but do not substantially increase overall local trip-making levels.
Methodology
survey
Sample size: 899
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
- Theoretical Contribution: computational model