Effects of Traffic Control Device and Hazard Location on Fixations During Intersection Navigation: A Simulator Study
Bibliographic record
Abstract
Collisions resulting in injuries or fatalities occur more frequently at intersections. This is partly because safe navigation of intersections requires drivers to accurately observe and respond to other road users with conflicting paths. Previous studies have raised questions about how traffic control devices and the positioning of other road users might affect drivers' visual search strategies when navigating intersections. To address these questions, four left-turn-across-path (LTAP) scenarios were created by combining two types of traffic control devices (stop signs and traffic lights) with two hazard starting locations (central and peripheral). Seventy-four licensed drivers responded to all scenarios in a counterbalanced order using a full vehicle driving simulator. Eye-tracking glasses were used to monitor eye movements, both before and after hazard onset. The results revealed that drivers at the signalized intersections took longer to fixate the LTAP hazard before onset, spent less time fixated on the hazard, and made fewer visits to the hazard compared to drivers at the unsignalized, stop-sign controlled intersections. There were no significant main effects of hazard location on the eye tracking measures, however, there was an interaction between the traffic control device and the hazard location on total fixation duration. Participants fixated the signal-controlled hazards longer in the central location but fixated the stop-sign controlled hazards longer in the peripheral location. Despite the inherent risks of intersecting traffic, results indicate the drivers at signalized intersections rely heavily on the traffic control device to prevent collisions. Given the high speed and traffic volume at signalized intersections, these results can be used to justify the implementation of measures such as protected left turn signals and other infrastructure changes aimed at reducing the frequency and severity of LTAP collisions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".