Methodical approach in determining the reliability and efficiency of urban cargo transportation taking into account the congestion of street networks
Bibliographic record
Abstract
Abstract A methodical approach has been developed in order to assess the reliability and efficiency of urban cargo traffic, taking into account the congestion of street networks. Modelling the transportation process is carried out online and allows one to determine the parameters of the transportation process, which take into account the presence of traffic jams on the city streets. A criterion for assessing the reliability of the logistics system of cargo urban transportation has been proposed – the reliability coefficient and the integral criterion of efficiency. The criteria take into account the travel time of the vehicle along the route and the time of delays in accepting the applications for services at the logistics centre (LC), as well as the time of delays at the transport company. It has been shown that in the absence of delays in the logistic chains, the reliability coefficient is equal to one, and the efficiency criterion tends to a minimum. The role of the LC capacity in the unit costs of transport services has been determined. Insufficient capacity of the LC increases the processing time of one application, which leads to an increase in the total unit costs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".