Harmonized Annual Averaged Traffic Data at Street Segment Level for European Cities
Bibliographic record
Abstract
Traffic flow data in Europe are collected locally by city authorities using different systems and standards, making it difficult to compare cities or evaluate large-scale maps, such as those used for emission inventories. To address this gap, we compiled and harmonized publicly available traffic data for 36 European cities, linking geolocalized information to road segments, spanning years from 2015 to 2024 depending on data availability. Annual Average Daily (or Weekday) Traffic is provided, and supplementary variables (e.g., truck flow percentages and speed metrics) are included where available. The data are georeferenced, with geometries corresponding to each measurement location. The dataset was enriched with additional attributes through map matching of traffic measurement locations to OpenStreetMap. Code and methodology for transforming raw data into a uniform structure are documented in Python Jupyter Notebooks, ensuring transparency and reproducibility. This dataset in a unified format facilitates cross-city comparisons and supports applications in environmental science, including the estimation of greenhouse gas and pollutant emissions, as well as urban planning and road transport management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".