Analysis of Message Delivery Delay in Vehicular Networks
Bibliographic record
Abstract
Improving the safety of roads and passengers is the main goal of intelligent transportation systems (ITS). However, reducing traffic and car accidents can only be achieved by disseminating safety information in a timely manner with high reliability. Although mathematical modeling of this process is extremely beneficial, analyzing the information dissemination is considerably complex due to the dynamics of vehicles and varying travel behavior. We present a mathematical model to study the delay-reliability features in a vehicular network. The proposed model not only captures both physical and medium access control (MAC) layers' characteristics of this network but addresses the partitioning problem, i.e., the tendency of cars to form disconnected islands, as well. To the best of the authors' knowledge, this is the first delay model that does all of these, and it can be used to understand the message propagation behavior in vehicular networks. The common assumption in the literature is that the intervehicle distances (IVDs) both inside the island and between islands have the same exponential distribution. We show that this assumption is flawed, and subsequently, a new and more accurate model is presented. Simulation studies validate the accuracy and effectiveness of the proposed model for both highway and urban scenarios.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".