Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture
Bibliographic record
Abstract
Drivers' reaction time of reading signs on expressways is a fundamental component of sight distance design requirements, and reaction time is affected by many factors such as information volume and concurrent tasks. We built cognitive simulation models to predict drivers' direction sign reading reaction time. Models were built using the queueing network‐adaptive control of thought rational (QN‐ACTR) cognitive architecture. Drivers' task‐specific knowledge and skills were programmed as production rules. Two assumptions about drivers' strategies were proposed and tested. The models were connected to a driving simulator program to produce prediction of reaction time. Model results were compared to human results in sign reading single‐task and reading while driving dual‐task conditions. The models were built using existing modelling methods without adjusting any parameter to fit the human data. The models' prediction was similar to the human data and could capture the different reaction time in different task conditions with different numbers of road names on the direction signs. Root mean square error (RMSE) was 0.3 s, and mean absolute percentage error (MAPE) was 12%. The results demonstrated the models' predictive power. The models provide a useful tool for the prediction of driver performance and the evaluation of direction sign design.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".