EVALUATION OF TWO METHODS OF PROMPTING DRIVERS TO USE SPECIFIC EXITS ON CONFLICTS BETWEEN VEHICLES AT THE CRITICAL EXIT
Bibliographic record
Abstract
The Florida Department of Transportation used a series of changeable-message signs that functioned as freeway guide signs to divert traffic to Universal Theme Park via one of two eastbound exits based on traffic congestion at the first of the two exits. An examination of crashes along the entire route indicated a statistically significant increase in crashes at the first eastbound exit following the actuation of the system. Furthermore, all of the crashes occurred in close proximity to the exit gore (the crosshatched area at exits that drivers are not supposed to enter or traverse) at the first exit. In Experiment 1, behavioral data were collected using an alternating treatments design. These data revealed that reassigning the exit signs was effective in producing a change in the percentage of drivers using each of the two exits. These data also showed that the reassignment of the theme park exit was associated with an increase in the percentage of motor vehicle conflicts that consisted of vehicles cutting across the exit gore. An analysis revealed that the method used for switching the designated or active theme park exit on the series of changeable-message signs led to the presentation of conflicting messages to some motorists, thus resulting in erratic driving behavior (cutting across the exit gore). In Experiment 2, the treatment evaluated the use of a phased method of switching the designated theme park exit to eliminate the delivery of conflicting messages. The new method for switching the designated theme park exit was not associated with an increase in motorists cutting across the exit gore.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".