Bumpy Rides: An Extensive Accelerometer-Based Cycling Infrastructure Survey
Bibliographic record
Abstract
Comfortable cycleways are key to the success of a cycling network. However, evaluating comfort on many cycleway links can prove challenging with regard to resource requirements. This paper evaluates cycling comfort using GPS- and accelerometer-equipped bicycles in Montréal, Laval, and Longueuil (Canada). The objective of the study was threefold. First, to present a framework for efficiently evaluating cycling comfort of many cycling infrastructure links (segments), by accounting for various sampling conditions (speed and cyclist characteristics). Second, we aimed to analyze how cycling comfort relates to cycling infrastructure type. Third, we sought to identify hot spots and cold spots of comfortable cycleway links within the study area. The results showed that the dynamic comfort index was significantly influenced by the characteristics of the cyclists themselves and by the speed at which they were traveling. Off-street bike paths were significantly less comfortable than shared lanes, bike lanes, and streets without cycling facilities. Laval had more than its share of high-comfort clusters, whereas Montréal had significantly more low-comfort clusters than its counterparts. These results should be used to improve cycleway planning and quality monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".