LETTER FROM THE GUEST EDITORS-Driving Behavior and Traffic Safety in Traffic Engineering
Bibliographic record
Abstract
Road safety has been internationally recognized as a primary strategic goal in many societies and involves road user, vehicle and environment elements in a systematic manner.The element of road users represents all aspects of human factors, such as user age, gender, health condition, personality, alcohol use, distraction, driving experience, etc.The vehicle element includes vehicle type, size, weight, design, capability, technology, etc while the element of environment involves roadway factors (traffic volume, road type, pavement/terrain surface condition, signage, traffic control device, sight distance, number of lane, speed limit, traffic volume, etc) and natural environment factors (such as weather and lighting conditions).The complex interactions among these factors reflect the complex driving behaviors on the roads.Clearly, better driving behaviors will result in the better traffic safety.While traffic safety is measured by numbers of accidents, injuries, or damages, it is not as observable as driving behaviors.Therefore, the theme of this special issue emphasizes that improving traffic safety requires a better understanding of the driving behaviors on roads and the interaction between driver, vehicle, and environment.Through this special issue, we aim to collect multi-method research outcomes in addressing driving behavior and safety issues.This special issue selects seven papers submitted from three countries: USA, Canada, and China.They represent diverse perspectives in exploring the relationships between traffic safety and driving behaviors.These studies embody the efforts of the traffic safety engineers and researchers on improving transportation safety and quality.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".