Non-motorized Vehicle Traffic Accidents in China: Analysing Road Users’ Precrash Behaviors and Implications for Road Safety
Bibliographic record
Abstract
The paper aims to explore underlying patterns of non-motorized vehicle (NM, including both regular bicycles and e-bikes) traffic accident occurrences based on precrash behaviors. A quarter-year data of NM accidents was collected by Yinzhou Traffic Police Department of Ningbo, China. Descriptive statistics and Rough Set theory were used to examine rules within different types of NM accidents from temporal, spatial, and behavioral aspects. Some main findings include: behavior patterns of different parties involved vary across different accident types, levels of roads, and intersections; motorized vehicle’s illegal turning as well as NM’s reverse riding are the two key behaviors that deserve concern across all levels of roads and intersection; in addition, for higher level urban roads more attention should be focused on lane violations of motorized vehicles, and for branch roads and intersections prevention efforts could be directed to motorized vehicles’ illegal turning around and NM’s red-light running respectively. Results from this paper could facilitate related staff formulating more targeted policies to make roadways safer.
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 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 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".