E-scooter riders: A cross-cultural analysis of traffic safety attitudes and behaviors
Bibliographic record
Abstract
The rapid adoption of electric scooters (e-scooters) has transformed urban mobility, offering a practical and flexible alternative to traditional transportation modes, particularly in areas with limited access to public transit. However, this rise in popularity has also brought about serious road safety concerns, particularly regarding risky behaviors such as riding under the influence of alcohol, carrying multiple passengers, and non-compliance with traffic regulations. While non-compliance with traffic regulations is not unique to e-scooter users, the combination of multiple risky behaviors observed among them may contribute to a higher likelihood of such violations. In addition, protective behaviors, such as helmet use, remain low among many riders, increasing injury risk in the event of a crash. This study aimed to analyze the prevalence of self-reported risky behaviors across various demographic groups and regions, and to assess factors contributing to the likelihood of unsafe e-scooter riding behavior. To achieve this, we used data from the third edition of the E -Survey of Road users' Attitudes (ESRA), focusing on responses from 39 countries worldwide. Descriptive analyses of self-reported data were conducted to examine e-scooter usage patterns and self-declared risky behaviors. Additionally, mixed-effects logistic regression models were employed to identify significant predictors of these behaviors, including gender, age, student status, crash history, and attitudes toward traffic laws. The results revealed that younger individuals and males are more likely to use e-scooters and engage in risky behaviors. Key factors influencing or associated with these behaviors included previous crash involvement, student status, and permissive attitudes toward safety regulations. The study highlights the need for targeted safety interventions that address infrastructural factors as well as behavioral factors, including demographic and attitudinal influences. This integrated approach can help policymakers develop more effective strategies to mitigate the risks associated with e-scooter use and enhance urban road safety.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".