Bibliographic record
Abstract
High Occupancy Vehicle (HOV) lanes are one [of] the most commonly used methods to reduce the number of vehicles on the road network. HOV provides a faster and reliable option to single occupancy vehicles, thus inducing more people to car pool. The success of HOV lanes depends on the reduction of travel time and increased trip reliability. Therefore, in order to reduce travel time and improve trip reliability this study emphasizes on the HOV access location relative to an access ramp. In this case we have chosen the interchange at Erin Mills Parkway and Highway 403 as a subject of our study. The study was divided into 2 parts, namely the field review and simulation of different options in order to optimize the HOV access location. During the field review it was found that 75% of the vehicles are in a position to enter the HOV lane 200m upstream of the exiting access location. Also, approximately 35% of vehicles were jumping the buffer before the start of the access location. In the second part of the study a total of 6 options were explored using VISSIM micro simulation software. The results of the simulation showed that the access location 200m downstream of the Speed Change Lane with a total access length of 600m is the best option. In addition, it was found that buffer separated HOV lane operate better then HOV lane without a buffer zone. This can be attributed to the increase of HOV lane for short trips, which increases the traffic volume on the HOV lane.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".