TRAFFIC FLOW MODEL WITH INFLUENCE OF PASSENGER TRANSPORT
Bibliographic record
Abstract
This work deals with accomplishing the first objective of the research. It is determined that passenger route traffic can be divided into traffic by city roads and traffic by country roads. Outside cities vehicles use mostly public roads with two or four lanes. Country roads with four lanes are characterized by the following features: there are no obstacles like crossroads with pedestrian crossings at the same level; traffic ban is applied to certain road users; top speeds can be formed; there are accidents with route vehicles involved. There is substantial analysis of the statistical data concerning accidents with route vehicles involved on public roads, and the accidents mentioned are characterized by significant severity. The number of people who are injured or die in the result of one road accident with a passenger route vehicle involved is significantly bigger as compared to other kinds of road accidents. The accidents under discussion are characterized by extreme severity in conditions of high speeds on public roads as compared to road sections of other types. All this demonstrates the relevance of the direction that has been chosen for scientific research.Passenger route traffic safety is indirectly taken into account only when the route is planned and the indicator is added to the route passport. The information that the route passport provides refers to existing rail crossings and traffic accident areas. These specified elements are entered into the route scheme and are cited as a list. At present there are no tools to calculate the degree of danger; also there are no methods to organize a route that takes into account passenger route traffic safety on route sections as part of the relevant traffic flows. With the existing accidents statistics and corresponding severity indicators taken into account, the scientifically practical objective to develop a method to estimate passenger route traffic safety on public roads is clear. In accordance with the task proposed, the object, the subject, the goal and objectives of the research have been formulated in the article.For every level of the traffic flow analysis, a theoretical basis for forming traffic safety of passenger route vehicles within their interaction with other vehicles in the traffic flow has been formulated. It has been proposed to reveal negative phenomena which precede road accidents and occur in the traffic flow due to passenger route vehicles traveling at a set speed and at set intervals. These phenomena should be represented as deviations in kinematic characteristics of the traffic vehicles flow from similar characteristics of the passenger route vehicles flow.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".