Assessing São Paulo’s public transport efficiency and coverage through data-driven modeling of autonomous vehicle and vehicle-as-a-service integration
Bibliographic record
Abstract
São Paulo, the largest Brazilian metropolis, faces complex challenges in urban mobility, exacerbated by growing population demands and the unequal distribution of public transport infrastructure. This study analyzed the efficiency of the city's public transportation system using geospatial visualization techniques and data analysis. Significant disparities were identified between central areas, which exhibit higher density and connectivity, and peripheral neighbourhoods, which experience lower vehicle frequency and longer waiting times. To address these challenges, the study explored the potential of emerging technologies, such as autonomous vehicles and the Vehicle-as-a-Service (VaaS) model, to expand coverage and enhance public transportation efficiency. Autonomous vehicles can help reduce accidents caused by human error and alleviate traffic congestion, while the VaaS model offers an intelligent integration of vehicles and urban infrastructure, fostering a more flexible and scalable mobility system. Despite the promising prospects of these innovations, their implementation faces several challenges, including the need for specific regulations, robust technological infrastructure, and public acceptance. This study concludes that adopting analytical technologies and emerging solutions is crucial to transforming urban mobility in São Paulo, fostering a more efficient, inclusive, and sustainable public transportation system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".