Reshaping cyclist mobility: Understanding the impact of autonomous vehicles on urban bicycle users
Bibliographic record
Abstract
Autonomous Vehicles (AVs) present a jarring new normal for negotiation on public streets. Current communication and interaction stand to be disrupted with the presence of AVs. Active transport users, specifically bicycle users, rely on human communication and subtle cues to feel confident when negotiating with other road users. Shifts in communicability with AVs presents a unique challenge for bicycle users. It remains unclear how AVs will impact urban bicycle users. This paper employs a mixed-methods approach to understanding impacts that AVs have on urban bicycle users in four test sites with varying levels of traffic stress. Interviews and focus groups were used across different traffic stress scenarios in three countries to understand how bicycle users will be impacted by AVs in real-world scenarios. The results represent a pioneering cross-section of four bicycle cultures’ exposure to AVs. Results universally show that, compared to conventionally operated vehicles, an enduring sense of unease remains unresolved with AVs and that shifting focus to bicycle user experiences and prioritizing the needs of a diverse population of non-occupants will be critical. If AVs are not deployed responsibly and responsively, they could turn bicycle users away from cycling in a streetscape where active transport users are already marginalized.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".