Bibliographic record
Abstract
High-occupancy vehicle (HOV) lanes are designed to increase the passenger-carrying capacity of roadways by requiring that all vehicles using the HOV lanes be occupied by a minimum number of passengers (e.g., the driver plus, at least one additional passenger). HOV lanes, in various forms, have been in use across North America for several decades. However, there is little information available with respect to their impact on the safety performance of the roadways to which they have been added. Over the past ten years, HOV lanes have been added to several freeway facilities in the Greater Toronto and Hamilton Area (GTHA), and analysis was recently undertaken to examine how the HOV lanes have impacted safety on the treatment sections of those roadways. Predictive collision analysis was used to calculate collision modification factors (CMF) for the addition of buffered, limited-access, concurrent flow HOV lanes to a simple, controlled-access freeway. Related, trends in collision activity have also been identified, as have areas for potential future study. Based on the CMF development and collision trend analysis, the general conclusion that can be reached is that the addition of buffered, limited-access, concurrent flow HOV lanes to a simple, controlled-access freeway can be expected to result in a moderate (15%) increase in overall collision frequency. The increase in collisions consists almost entirely of additional property damage only (PDO) collisions, particularly rear-end collisions, freeway congestion and several HOV lane design elements appear to be contributing factors.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".