Coupled evaluation of communication system loading and ATIS/ATMS efficiency
Bibliographic record
Abstract
Significant efforts are being made at present to define, evaluate, and ultimately deploy, various forms of intelligent transportation systems (ITS). These systems permit the implementation of advanced traffic control strategies through the application of advanced surveillance, control and communication systems. Traditionally, the evaluation of the performance of these communication systems and traffic networks has been performed predominantly independent of each other. This paper describes the development and application of an extension to an ITS benefits evaluation model to permit a partially coupled evaluation of the communication system loading and the ATMS/ATIS functions. This extended model is capable of estimating dynamic local and network wide communication loadings that depend on the spatial and temporal traffic demands, the network topology, the characteristics of the communication hardware and the communication system operating rules. The model is applied in this paper to a simple hypothetical network to demonstrate the potential benefits of carrying out a partially coupled evaluation of communication system loading and traffic network efficiency. A sensitivity analysis was carried out to determine the impact of the fraction of ITS equipped vehicles and the level of congestion on the level of communication loadings. It was demonstrated that, as a result of congestion and traffic diversion, antenna communication loads did not uniformly increase in direct proportion to the average number of equipped vehicles entering the network.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".