Impact of Dynamic and Safety-Conscious Route Guidance on Accident Risk
Bibliographic record
Abstract
Under intelligent transportation systems, dynamic route guidance systems (DRG) provide routing information to motorists based on current traffic conditions on a network. Not enough attention, however, has been given to the impact of such dynamic routing decisions on network safety in terms of the predicted number of accidents. The objectives of this paper are to investigate the variation of network-wide accidents caused by traffic redistribution subject to various levels of DRG market penetration, and to examine the potential of a new safety-enhanced route guidance system. A microsimulation model was developed and integrated with a set of accident prediction models for links and intersections. Accident estimates were plotted against time to produce an accident profile that could describe the change of accident occurrence over a time period. Accident profiles, together with average travel time, were used to explain the relationships between DRG market penetration and the number of network-wide accidents. The integrated simulation model was also applied to enhance DRG by suggesting routes with the fewest estimated accidents and hence making route guidance safety conscious.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".