Empirical Evaluation of Drivers’ Route Choice Behavioral Responses to Social Navigation
Bibliographic record
Abstract
Even though route choice behavior and drivers’ acceptance of advanced traveler information systems have been studied in the past, little or no attention has been given to the route choice behavior and acceptance response to social navigation systems. What separates social navigation systems from traditional traffic navigation is that the route advice aims to minimize the individual travel time and the marginal total travel time in the network. In this study, drivers’ behavioral responses to social navigation route guidance were empirically evaluated under different information and incentive strategies. A traffic navigation application based on social navigation was developed and used in a pilot multiuser laboratory experiment. Participants were asked to make route choices in a virtual travel environment under various information and incentive strategies. Drivers were more willing to comply with the social advice when they were well informed and well rewarded. The results also show that female and novice drivers are more willing to comply with the social advice than are male drivers and experienced drivers. Aside from the level of altruism, a driver's indifference to switching routes also affects a driver's compliance with social advice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".