Proactive Control of Transport Flows of the Ramps-Mainline System in Intelligent Transportation and Logistics Systems
Bibliographic record
Abstract
With increasing traffic on motorways of international transport corridors, the demand for modern traffic control systems is generated. Based on the METANET model analysis and on-ramp control algorithms used in the United States, Canada, Australia, the Netherlands and several Asian countries: Fixed-time ALINEA, BOTTLENECK, ZONE, Demand Capacity (DC), VSL, HERO / RWS, PRMA, METALINE, SZM, SWARM, RPROP, DynaTAM etc., an algorithm is designed. This algorithm exploits the potential of Intelligent transportation and logistics system (ITLS) to implement proactive traffic control on the basis of its monitoring, forecasting intensity and direction, which allows one to anticipate the overloaded sections of the highway and ramps, develop control actions for the entrance traffic lights of ramps, traffic lights and placards that form the flows coming to the ramps, as well as recommended speeds and flow breaks in the entrance lane of the Expressway. The possibility of using self-driving cars to supplement information about traffic characteristics obtained from stationary sensors of the ITLS transport infrastructure is analyzed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".