Validation of an Outdoor Coast-Down Test to Measure Bicycle Resistance Parameters
Bibliographic record
Abstract
Bicyclist rolling and aerodynamic resistance parameters are needed to estimate speed and energy expenditure in various travel analysis applications. These parameters have been investigated for sport and professional bicyclists, but better understanding is needed for real-world urban bicyclists. This paper describes a field coast-down test to measure bicycle resistance parameters that can be administered during traveler intercept surveys and generate representative data for advanced bicycle travel models. Mathematical models are developed that expand on past methods by accounting for varying wind and grade and allowing for increased measurement locations per test. A 12-sensor, 100-m test setup is developed, and indoor and outdoor validation tests are performed. The additional measurement locations yield higher precision than the previous three-sensor methods, but as expected, the precision of outdoor tests is lower due to inconsistent wind, grade, and riding surface. Outdoor validation tests generate rolling resistance coefficient estimates of 0.0064±0.0013 and effective frontal area estimates of 0.63±0.11 m2. Outdoor tests in a headwind are sufficiently sensitive to identify significant changes in resistance with riding position and tire pressure and are expected to generate realistic parameter estimates for parsimonious modeling of on-road bicyclists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".