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 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".