Pedestrian Fatality Data Quality: Problems and Definitions
Bibliographic record
Abstract
Accurate data on pedestrian fatalities is of upmost importance to public health officials, transportation planners, police, and policy-makers. It is used to make strategic decisions about when and where to invest scarce resources to eradicate preventable deaths and improve safety for all modes. The authors analyzed data from one year of pedestrian deaths in New Jersey, the US state with the highest share of pedestrian deaths, and found that the data is severely lacking. Roughly one quarter of the 157 pedestrian deaths reported in New Jersey in 2012 should not have been classified as pedestrians. Some of these fatalities should not be classified as pedestrians using the reporting definitions required by the National Highway Traffic Safety Administration (NHTSA), including some that are outright errors. Other fatalities are consistent with NHTSA’s definition of a pedestrian, but are questionable from a planning and data analysis perspective, as most planners and decision makers would not consider the victims to be pedestrians. The authors discuss these alternate definitions and classify each fatality accordingly. Implications for research and planning are discussed and we emphasize the need to both improve data collection and management, as well as for NHTSA to reconsider how they define and track pedestrian fatalities.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".