An investigation of freeway capacity before and during incidents
Bibliographic record
Abstract
This paper investigates freeway capacity before and during incidents. Data were obtained for approximately 1 year from five freeway facilities in North America. The data included flows, speeds, weather conditions, as well as incident information (location, duration, and lanes affected). Maximum throughput-related values were obtained to estimate capacity under non-incident conditions as well as under incident conditions. Under non-incident conditions, the data indicate that three-lane freeways are the most efficient in terms of per lane capacity. Measurements of capacity during incident conditions are provided by type of facility and number of lanes affected. These capacities are compared to values reported in previous research. Next, two sets of multiple linear regression models were developed to estimate the capacity under incident conditions and the capacity reduction (i.e. the difference between capacity under non-incident conditions and capacity under incident conditions) during incidents. Each of the two sets of models is developed for the three sites combined and for the Portland site on its own (because it has detailed information on the number of lanes blocked by incidents), based on factors such as the incident category, and the total number of lanes.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".