Naturalistic Data Analysis: Assessing Factors Impacting E-bike Cyclist Safety on Urban Roads
Bibliographic record
Abstract
Micro-mobility, particularly cycling, plays a vital role in urban transportation. Despite the advantages of electric-assist bicycles (e-bikes) and cycling, safety concerns remain a barrier to widespread adoption, specifically in urban traffic. This study builds upon previous in-field experimental research in Oshawa, Canada, to examine factors influencing lateral distance of overtaking motorized vehicles and e-bike cyclist safety and stress levels when sharing roads with vehicles. Participants (n=32) from varying age groups and skill levels underwent a 12 km cycling test, equipped with sensors to monitor vehicle proximity, cyclist dynamics, and physiological responses. Data analysis on overtaking minimum lateral distances revealed that type of vehicles affect the clearance at low speeds (less than 15 kph) and high speeds (20-25 kph). Also, traffic load and proximity of overtaking vehicles correlated with heightened cyclist heart rates at medium speeds (15-20 kph). Furthermore, large vehicles were found to significantly elevate cyclists' heart rates during overtaking maneuvers. This study contributes a framework for integrating smart technologies into urban cycling safety initiatives, leveraging real-time data to enhance situational awareness for both cyclists and drivers, while they share the road. Future research should focus on developing e-bikes safety features and demographic-specific interventions to further mitigate safety risks.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".