Investigating temporal trends in risk factors related to injury severity of crashes with pedestrians in urban areas
Bibliographic record
Abstract
OBJECTIVE: The implementation of road safety policy in urban areas can potentially change the severity profile of crashes, as well as how risk factors influence crash severity. In this sense, this study aims to empirically evaluate possible changes in the severity profile of crashes with pedestrians and in the influence of risk factors for pedestrian injuries after the efforts of the Decade of Action for Road Safety in the city of Fortaleza, Brazil. METHODS: This was done using data from crashes with pedestrians between 2009 and 2019; divided into three periods. Two categorical modeling analyses were performed using the mixed logit modeling approach, including sociodemographic, environmental, vehicle, road type, and traffic control device factors. In the first analysis, a single model was estimated, and time (period) was included as an explanatory variable; in the second one, models were estimated for each period. RESULTS: According to temporal analysis, a reduction was evident in the severity profile of crashes with pedestrians over the decade of action. In general, the safety interventions seemed to have little or no impact on pedestrian gender, young pedestrians (up to 15 years old), crashes at night, crashes during weekends and crossings near traffic lights. Regarding crashes on arterial roads, the results suggest an increase in the marginal effects for fatal crashes after the decade of action, while other variables, such as heavy vehicles and expressways, showed positive marginal effects in all periods, indicating that the direction of their effect did not change. This is a potential indication that the overall safety impact of policies during the decade were not effective for these types of crashes. It was possible to identify considerable reduction in the marginal effects for older pedestrians (60+) for both severe and fatal crashes. CONCLUSION: Although it is not possible to claim that this change comes from specific actions or controlled factors, the results presented here indicate an improvement in road safety for these users, in line with the goals of the Safe Systems Approach and the Decade of Action for Road Safety to reduce severe and fatal traffic injuries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".