Safety Related Behaviors and Law Adherence of Shared E-Scooter Riders in Germany
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it