A methodological approach for studying public bus transit driver distraction
Bibliographic record
Abstract
The increase in bus transit ridership along with the proliferation of personal electronic control and communication gadgets is causing more distractions for the drivers.For transit vehicles, some distractions are caused by factors beyond the driver's control such as operating additional equipment, attending to passengers, and communicating with the operations center.Several driver distraction studies have been conducted for personal vehicles and commercial vehicles.But bus transit driver distraction has received limited attention in the literature even though bus transit accidents may cause more injuries due to larger number of passengers.Hence, their distraction is not clearly understood; furthermore, no established methodology is available to conduct a detailed study at a transit agency because of inadequate research in the field.The objective of this paper is to present a detailed modular research framework for studying bus transit driver distractions.The framework provides a transit agency with a set of standardized methodologies for studying distraction over a wide range of cost and time intervals.An agency may choose one or more modules to suit their study requirements.The modules for data collection, analysis, validation, and interpretation and usage of results are designed on the basis of in-depth studies and tests at transit agencies in the Commonwealth of Virginia.The paper provides a detailed process and a set of guidelines to study bus transit driver distraction which will make it easier for any transit agency to conduct such a study.The results of the bus transit driver distraction studies could be used for training bus drivers to mitigate distraction and assist state and city governments to formulate effective regulations to control distracted driving.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".