Safety Related Behaviors and Law Adherence of Shared E-Scooter Riders in Germany
Bibliographic record
Abstract
Shared e-scooters, whose supply and coverage keeps increasing in many cities around the globe, are rapidly changing mobility in urban road environments. As rising injury rates have been observed alongside this new form of mobility, researchers are investigating potential factors that relate to safe/unsafe e-scooter use. In Germany, e-scooter sharing platforms were only recently permitted in the middle of 2019, and their number has increased steadily since then. The aim of this study was to assess key factors that relate to their safe use, through a direct observation of e-scooters conducted at three observation sites around Berlin. Helmet use, dual use, type of infrastructure use, and travel direction correctness were registered for 777 shared e-scooters during 12.5 h of observation. Results reveal a high level of rule infractions, with more than one quarter of observed shared e-scooter riders using incorrect infrastructure, and one in ten e-scooter users riding against the direction of traffic. Dual use (i.e., two riders per e-scooter), was observed for 5.1% of shared e-scooters. Moreover, none of the riders observed in this study used a helmet on their shared e-scooter. These results point to a need for better communication and enforcement of existing traffic rules regarding infrastructure use and dual use. Further, they indicate a lack of efficacy of safety-related advice of shared e-scooter providers, who promote helmet use in their smartphone application and directly on their e-scooters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".