Impact of Dynamic and Safety-Conscious Route Guidance on Accident Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it