Drivers’ Yielding Behavior in Different Pedestrian Crossing Configurations: A Field Survey
Bibliographic record
Abstract
Although in recent years road victims have been reported to decrease, the growing number of pedestrians involved in road accidents still remains a social concern. This work analyzes the drivers’ behavior in approach to two different configurations of pedestrian zebra crossing: marked by (1) white stripes over the pavement (named “white zebra crossing”) and (2) white stripes on a red-colored pavement (named “red and white zebra crossing”). Even though the latter configuration is nowadays quite widespread on urban environment, there is no scientific evidence of its actual effectiveness in conditioning drivers’ yielding behavior. This study was aimed at verifying the effectiveness of the red and white zebra crossing on improving road safety at pedestrian crossings. A set of synchronized cameras were used to record drivers’ behavior while approaching the pedestrian crossings. By reconstructing the speed profile of each surveyed driver (309 in total), it was possible to analyze the driver-pedestrian interaction. Data were used to study the driver yielding behavior, to analyze how it is affected by vehicle dynamic constraints, and to identify the significant explanatory variables of a logistic regression model for predicting the drivers’ likelihood of yielding the pedestrian on the different crossing configurations. As a result, significant differences in terms of yielding behavior on the two pedestrian crossing configurations were observed: a higher yielding rate (about 20% higher) and a higher tendency to yield to the pedestrian were reported for the red and white zebra crossing, especially for the most critical conditions of driver-pedestrian interaction. Moreover, the analysis of yielding behavior with respect to vehicle dynamics constraints highlighted that drivers approaching the red and white zebra crossing experienced more opportunities to yield. As a confirmation, logistic regression model showed that the yielding likelihood is significantly and positively affected by the presence of the red and white zebra crossing configuration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".