Microscopic Congestion Detection Protocol in VANETs
Bibliographic record
Abstract
Effective transportation status surveillance imposes critical challenges for the Intelligent Transportation System (ITS) design. In this paper, the microscopic congestion detection protocol (MCDP) is proposed to make the vehicle-to-vehicle (V2V) communication capable of monitoring vehicle density and identifying traffic jam. By introducing transportation control domain in the existing network protocol header, each vehicle can count its neighbors and estimate the time spacing among vehicles. MCDP provides an infrastructure-less solution to the estimate of vehicle density, flow, and average velocity in a microscopically manner. Moreover, the safety speed limit is introduced to make each vehicle calculate its time to cover the intervehicle distance, such that every vehicle is able to assess the transportation congestion by comparing with some predefined safety time threshold. Monte Carlo simulations of the MCDP over four typical Chinese highways are presented to compare the MCDP scheme with the existing Green-Shield congestion detection scheme. In addition, real road traces generated by SUMO over NS2 are utilized to show the achieved performance in terms of throughput, end-to-end delay, and packet delivery rate (PDR) in comparison to DSR and AOMDV in IEEE 802.11p and IEEE 802.11ac scenarios. On the basis of all the results, we conclude that MCDP is an inexpensive transport congestion detection technique for Vehicular Ad Hoc Networks (VANETs).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".