Improving the Accuracy of the Spatial Transferability of Direct-demand Models for Bicycle Volume Estimation at Intersections
Bibliographic record
Abstract
Direct-demand (DD) models are used to estimate bicycle exposure (typically expressed as annual average daily bicycle volume [AADB]) when observed counts are unavailable at a site. The DD models typically estimate exposure as a function of site characteristics, such as the geometry, surrounding land use, and sociodemographic characteristics. Developing a DD model requires observed volume counts and associated site characteristics data for many sites in the target jurisdiction. In the absence of this data, it is desirable to apply a DD model from another jurisdiction. However, the naïve transferability of DD models results in AADB estimates with large errors. This paper investigates the use of calibration methods to enhance the spatial transferability of DD models to estimate bicycle volumes at intersections. This paper examined five DD models across four jurisdictions: (1) City of Milton (52 sites); (2) City of Toronto (28 sites); (3) Region of Waterloo (158 sites) in Canada; (4) Pima County (70 sites); and (6) Arizona, US, covering a total of 308 sites. Five local calibration techniques were evaluated for their effectiveness in mitigating errors in naïve estimates. The findings indicate that calibration, particularly regression-based methods, significantly improves the accuracy of the AADB predictions, with calibration Model 3 being the most effective for jurisdictions with less than 80 count sites ( k < 80 ). For k = 5 , Model 3 reduced error by 56%, and Models 3 and 5 achieved up to an 80% error reduction at k = 30 . As more sites became available, Model 5 emerged as the superior calibration method for k ≥ 80 .
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".