Analysis of Traffic Flow in Coordinated Sections of Urban Road Networks by Application of up-to-date Low-Cost Methods
Bibliographic record
Abstract
Traffic lights are used around the world to control traffic flow in urban road networks in an efficient way. Especially in higher-ranking road sections of road networks, neighbouring intersections with signal control are normally coordinated between each other in order to serve traffic volume on a high quality level. In periodical time intervals, the coordination of signalised intersections needs to be analysed. In this way, it is possible to identify defects in signal control and to adapt traffic control to changed conditions. In this paper, a method for the “Analysis of Traffic Flow in Coordinated Sections of Urban Road Networks by Application of up-to-date Low-Cost Methods” will be presented. The developed method can be used for assessment of traffic flow as well as for identification of defects in signal control in coordinated road sections. It is a stand-alone solution which can be applied independently from local technical equipment of traffic lights. It is expected that the application of the developed analysis method will contribute to a better quality of traffic flow in urban road networks in European as well as in Asian countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".