The circulation of bicycles in Barcelona, street by street
Bibliographic record
Abstract
This report presents a first estimation of the volume of bicycles and scooters moving through Barcelona, street by street. Using advanced modeling techniques, we estimate the Average Annual Daily Bicycle Traffic (AADBT) at the street segment level for over 20,000 road segments. We rely on data from automatic counters managed by the city council as well as data collected by volunteers and civil society. The most accurate model, XGBoost, allows visualization of bicycle flows across the entire city and is a valuable tool for urban planning, infrastructure improvement, and road safety studies. One of the study's main achievements has been to demonstrate how citizen participation improves the quality and representativeness of the estimates: thanks to the work of 43 volunteers, it was possible to cover street typologies and areas outside the reach of automatic counters. Despite these improvements, the model still presents some overestimations, especially on streets without cycling infrastructure. Nevertheless, this beta version offers a solid foundation for future developments. This project illustrates the potential of citizen science and interdisciplinary collaboration to advance towards more sustainable, equitable, and evidencebased mobility in Barcelona.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".