Peculiarities of Regulating the Movement of Vehicles on Specific Sections of the Street and Road Network of Cities with Heavy Traffic Flow
Bibliographic record
Abstract
Potentially dangerous sections of the street and road network of the city of Calgary (Canada) were analyzed and identified based on our own observations. It was established that individual sections of the street-road network form cross traffic flows that create conflict points of confluence and crossing. The small distance between the junction elements leaves drivers with insufficient time to maneuver, which increases the risk of traffic accidents. The described transport problem is typical for large cities with a developed road transport network and high traffic intensity. Recommendations for traffic regulation on the street-road network of cities with high traffic intensity and speed of traffic flow have been developed. A proposed solution to the problem of adjacent traffic flows changing lanes on a short section of the street-road network. The essence of the proposed solutions to the described transport problem involves both traditional approaches (building additional traffic lanes for maneuvering) and alternative approaches - changing the existing traffic organization using road markings and priority signs, which does not require significant financial investments. The peculiarity of the proposed solutions is that they provide that the traffic flow moving along the traffic lane that merges with the main traffic flow should not reduce the speed of traffic, which will contribute to its merging with the main traffic flow. Another option involves the use of active cruise control systems on vehicles. The proposed solutions should prevent the occurrence of traffic accidents on the considered and similar sections of the street-road network of cities with intensive traffic flow.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".