A flexible control study of variable speed limit in connected vehicle systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traffic congestion has already been a distinctly serious problem in both developed and developing countries. Among the proposed methods to solve the traffic congestion problem, variable speed limit (VSL) is considered as one of the most promising methods. But to the traditional VSL, the speed limit sign and the control distance is fixed, which makes VSL lack deployment and control flexibility. Whereas, in connected vehicle (CV), which is a crossing field of intelligent transportation systems (ITS) and internet of things (IoT), control and deployment flexibility can both be achieved. Furthermore, a big data environment is formed in CV to solve the traffic problems. In this paper, connect vehicle-based variable speed limit (CV-VSL) is proposed, and a simulation platform SimIVC is used to study the influence of control distance. The results show that the improvement of traffic performance increases 0.72% when the control distance is 270 metres than that of 250 metres, which means that the traffic performance can be further improved by CV-VSL.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it