Potential impact of autonomous vehicles on movement behaviour: A scoping review
Bibliographic record
Abstract
Purpose: To explore the potential impact of autonomous (i.e., driverless) vehicles (AV)s on movement behavior (i.e., physical activity, sedentary behavior, sleep). Guided by ecological models of health behaviour, we conducted a scoping review of the literature as it related to AVs and impact on movement behavior (MB) or mode choice (e.g., public transit), beliefs about MB or mode choice, or impact on environments that may influence MB or mode choice. Method: An extensive search revealed 933 possible studies which were then reduced to 77 after a title and abstract scan and to 22 after a full-article scan. The studies were then coded by two reviewers for characteristics of the design, participants, and findings. The purpose and main findings were recorded as text and subjected to thematic analyses. Results: The majority of studies took place in Europe or North America and involved cross-sectional or qualitative designs. The bulk of the research examined impact of AVs on the built environment (e.g., reduced demand for parking) and/ or mode choice (e.g., shift from public transit to shared AVs). Almost no research examined direct influences of AVs on movement behavior. Conclusion: Though no experimental studies have been conducted, the findings from the reviewed studies suggest that AVs will have a profound impact on the built environment and mode choice of people residing in much of the developed world. As a result, the movement behavior of residents in urban areas will be altered. We speculate that people will take fewer steps on a daily basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".