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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".