Evaluation of driver satisfaction of travel information on variable message signs using fuzzy aggregation
Bibliographic record
Abstract
Abstract Driver satisfaction regarding travel information provided by variable message signs (VMS), which are part of the Nam‐Mountain Tunnel ATIS, was evaluated using fuzzy aggregation. Application of fuzzy aggregation to analyze driver satisfaction allows one to represent the variability and complexity of human perception with great fidelity. A fuzzy weighted average using two sets of fuzzy membership functions was applied to evaluate individual satisfactions of delay and travel time information provided. Then, those individual satisfactions were aggregated to estimate the driver group's overall satisfaction. The evaluated overall satisfaction was 0.65 for delay information and 0.63 for travel time information. Through these results, it was found that users of the travel information provided by the VMS in the Nam‐Mountain Tunnel ATIS were somewhat satisfied with the service quality. Those overall satisfactions were compared with a conventional weighted average and traffic operational effects to demonstrate the usefulness of the developed fuzzy method.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".