Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes
Bibliographic record
Abstract
A hit-and-run (HR) crash occurs when the driver of the offending vehicle flees the crash scene without reporting it or aiding the victims. The current study aimed at contributing to existing literatures by comparing factors which might affect the crash severity in HR and non-hit-and-run (NHR) crashes. The data was extracted from the police-reported crash data from September 2017 to August 2018 within the City of Chicago. Two multinomial logistic regression models were established for the HR and NHR crash data, respectively. The odds ratio (OR) of each variable was used to quantify the impact of this variable on the crash severity. In both models, the property damage only (PDO) crash was selected as the reference group, and the injury and fatal crash were chosen as the comparison group. When the injury crash was taken as the comparison group, it was found that 12 variables contributed to the crash severities in both HR and NHR model. The average percentage deviation of OR for these 12 variables was 34%, indicating that compared with property damage, HR crashes were 34% more likely to result in injuries than NHR crashes on average. When fatal crashes were chosen as the comparison group, 2 variables were found to be statistically significant in both the HR and the NHR model. The average percentage deviation of OR for these 2 variables was 127%, indicating that compared with property damage, HR crashes were 127% more likely to result in fatalities than NHR crashes on average.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".