Perceptions toward pedestrians and micromobility devices in off-street cycling facilities and multi-use paths in metropolitan Vancouver, Canada
Bibliographic record
Abstract
• Motorized PMD are perceived more prevalent and faster than they actually are. • Despite misperceptions, people are generally comfortable with almost all PMD. • Pedestrians are less comfortable sharing with electric PMD than non-pedestrians. • Electric motor on a PMD has an equivalent effect on comfort to a non-motorized PMD going 8.5 km/hr faster. As micromobility or personal mobility devices (PMD) expand and evolve for urban transportation, there is increasing concern about the comfort and safety of off-street cycling facilities and multi-use paths. While there is a growing literature on the impacts of electric bicycles and scooters, we lack a comprehensive understanding of the comfort implications of the diverse array of micromobility devices currently in use. In this study we 1) characterize perceptions of comfort for people sharing off-street transportation facilities with pedestrians and 24 types of PMD, 2) examine how perceptions of comfort are affected by the perceiver, device, and contextual variables, and 3) investigate the disparity between actual and perceived micromobility mode shares and speeds. We collected data using a quasi-intercept survey of path users at 12 locations in metropolitan Vancouver, Canada, which were matched with location-specific volumes and speeds classified by PMD type. Results indicate that, despite an overestimation of the prevalence and speed of new forms of PMD, people are predominantly comfortable sharing with most devices except sit-down electric (moped-style) scooters. Pedestrians are less comfortable sharing with electric devices than are people using other PMDs. Model results show that motorized PMD would have to go 9 km/hr slower than the equivalent non-motorized device to have the same impact on comfort. Recommendations include modernizing PMD regulations, working to eliminate the use of sit-down electric scooters on off-street facilities, lowering thresholds for separating pedestrians on multi-use paths, and continued monitoring of speed and comfort impacts from evolving PMD.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".