What is on the Bicycle Paths? A Detailed Vehicle Taxonomy with Mode Share Data for Off-Street Paths in Metropolitan Vancouver, Canada
Bibliographic record
Abstract
The trend of increasingly multi-modal urban transportation has accelerated with the advent of low-power vehicles such as electric-assist bicycles, scooters, and skateboards. Despite increased attention, little is known about the actual usage of these vehicles, such as their mode shares in off-street and cycling facilities. The objectives of this study are to: (1) catalog all motorized and non-motorized vehicles in use on cycling facilities in metropolitan Vancouver, Canada; (2) create a taxonomy of the vehicles using visually identifiable features; and (3) determine volumes and mode shares for each vehicle type. We develop and validate a field data collection method using pneumatic tubes synchronized with video cameras, and collect classified volume data at 12 strategically selected locations over four seasons. A total of 25,282 vehicles are classified from more than 450 h of video data, categorized into 27 types using 10 features, such as number of wheels, number of axles, and existence of a battery/motor. Conventional (non-shared) bicycles are still the dominant vehicle in use, with a mode share of 90%. Although a variety of motorized personal mobility devices are present in cycling facilities, their mode shares are still extremely small. Electric bicycles are the most common motorized vehicle, with a mean mode share of 4% (ranging from 0% to 20% by location-date). The proposed taxonomy enables consistent empirical data collection to evaluate mode share trends and patterns, and to study the impacts of regulatory changes, incentive programs, and new infrastructure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".