An attempt to determine the impact of the implementation of autonomous vehicles on a larger scale on the planning of city transport systems
Bibliographic record
Abstract
Purpose: This paper aims to explore the potential impact of autonomous vehicles (AVs) on urban planning, sustainable urban development, and tourism. Methodology: The paper is a conceptual study that reviews and synthesizes existing literature on AVs, urban planning, and tourism. It also uses case studies to illustrate the potential effects of AVs. Results: The widespread adoption of AVs is likely to have significant implications for urban planning, including changes in land use, infrastructure design, and transportation patterns. AVs may also contribute to sustainable urban development by reducing traffic congestion and air pollution. In the tourism sector, AVs could lead to spatial changes, new social inequalities, and changes in the overnight visitor economy. Theoretical contribution: The paper contributes to the understanding of the complex interplay between AVs, urban planning, and tourism. It highlights the need for urban planners and tourism stakeholders to consider the potential impact of AVs in their decision-making processes. Practical implications: The paper provides practical insights for urban planners, tourism stakeholders, and policymakers on how to prepare for and adapt to the widespread use of AVs. It also emphasizes the importance of considering the potential benefits and challenges of AVs in the context of specific cities and tourism destinations.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".