Motorcycle Ban and Traffic Safety: Evidence from a Quasi-Experiment at Zhejiang, China
Bibliographic record
Abstract
Motorcycle bans have been implemented in many cities across China for long time, one of the main reasons for which is the high death rate of the traffic accidents related to motorcycles’ fast speed and weak safety. This study applies a quasi-experiment on whether or not and when motorcycle bans are implemented in the 11 prefecture cities in Zhejiang Province, taking the prefecture-level cities with motorcycle bans as the experimental group and the others as the control group, so as to identify whether such bans can effectively reduce the number of traffic accidents and deaths, as well as the related internal mechanism. This study concludes that the effect of the motorcycle bans on reducing the number of traffic accident deaths is significant, and their impact does not decrease over time due to the diversity of policies. Further, the mechanism analysis shows that the motorcycle bans have not only reduced the number of motorcycles and thus may improve the traffic safety but also diminished the traffic accidents by reducing the fatality rate. Finally, this study proposes to optimize the motorcycle bans by planning special lanes and strengthening motorcycle management.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".