Bicycle Parking Use Patterns, Occupancy and Rotation Rates in the Streets of Barcelona
Bibliographic record
Abstract
Bicycle parking is a critical piece of cycling infrastructure and yet there is little known about who and how often these facilities are used. This study investigates the use patterns, occupancy, duration times and rotation rates of on-street bike parking in Barcelona, Spain. We sampled all city districts with rectangular plots (20 ha) that included 163 bike parking locations. We visited them 4-15 times during two field work campaigns in spring and summer 2021. We measured total occupancy, bicycle type (bike-share, kid, abandoned, private) and developed a method to estimate rotation rates, turnover and service levels. We estimate that Barcelona has approximately 18,730 bicycles parked on the street every day, of which 8% are dockless shared bikes, 5% are kid bikes and 3% are abandoned bikes or bike parts. We find that 10% of bike parking locations were saturated (average occupancy 90%+) and 30% reached saturation in at least one measurement. Contrary to our hypothesis, we do not find strong diurnal temporal variations, as occupancy rates are remarkably stable over the day across the city. We estimate that 35% of bicycles parked on the streets are for long term parking (1 week+) or storage, while only 12% of bicycles are short-term day-users. To improve the management of on-street bicycle parking, cities must maintain an updated spatial data set of bike parking locations and understand the patterns of who and when they are used.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".