Bibliographic record
Abstract
The effects of light rainfall on urban freeway operations are modeled to improve understanding of road safety and speed-flow-occupancy relationships in suboptimal weather. Three broader issues also are addressed. What is the form of the relationship among various traffic variables under rainy versus dry conditions? Are the safety implications of driver adjustments under rainy conditions different for the day and night? How should speed variation be measured in ecological studies? Volume-occupancy and speed-volume relationships are affected by rainfall: specifically, speeds are reduced and speed is more strongly dependent on volume. Under nighttime, rainy, uncongested conditions, speeds are reduced and time gaps are increased, but only minimally. Under daytime rainfall conditions, when traffic volumes are typically high, speeds are reduced substantially, and because of the interaction between traffic variables, volumes also decrease. The physical time gap increases marginally as well, whereas speed variability within the traffic stream is reduced. For congested daytime conditions, light rainfall is not associated with any changes in volume or time gap, but speeds are reduced. Finally, safety-related information on speed deviation can be derived from traffic loop data by calculating the variability of travel speeds within small time units.
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.
How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".