Modeling Driver Psychological Deliberation During Dynamic Route Selection Processes
Bibliographic record
Abstract
Dynamic route guidance (DRG) is an ITS application targeted to reduce the prolonged daily periods of severe congestion. The success of such system relies on its ability to disseminate reliable pieces of information to travelers in real time. The predictive accuracy of disseminated information requires a realistic understanding and representation of drivers' behavior and more specifically their route choice decisions and processes. Accordingly, this research attempts to step out of the engineering borders to the psychological arena through adopting one of the most successful decision theories; decision field theory (DFT). The choice mechanism of DFT is based on the simulation of the evolution of decision-makers preferences through out the deliberation process reflecting a process-oriented modeling approach. This study presents a modeling framework for drivers' decision making process based on the theoretical foundation of DFT. Three scenarios are discussed that vary in the level of traveler information presented to the driver, namely; no information, descriptive information (congestion states) and prescriptive information (specific guidance). Due to the highly intertwined elements of the theory and resulting model framework, an overly simplified application is used for illustration purposes
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".