The effects of distractions and driver’s age on the type of crash and the injury severity sustained by occupants involved in a crash (Master dissertation). Retrieved from University of Toronto theses database: http://hdl.handle.net/1807/32601
Bibliographic record
Abstract
This thesis investigates the associations between crash outcomes, the existence and type of driver distraction as well as driver’s age. Only a limited number of studies have investigated driver distraction as it relates to crash outcomes. Moreover, these studies were limited to specific demographics (i.e., young drivers and police officers). This thesis addresses a gap in the literature by considering and comparing drivers of all ages. The crash outcomes considered in this thesis consist of the type of crash as well as the injury severity sustained by occupants (i.e., passengers and drivers) involved in the crash. An ordered logit model was built to predict the likelihood of severe injuries and a multinomial model was developed to predict the likelihood that a driver will be involved in one of three common crash types: singular, angular, and rear-end. The models were built on a national crash database: U.S. General Estimates System (2003 to 2008). In these models, various factors (e.g., weather, driver’s gender, and speeding) have been statistically controlled for, but the main focus was on the interaction of driver’s age and distraction type. The findings of this thesis have implications for policy making and prioritizing capabilities of distraction-related safety systems.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".