<scp>AI</scp>‐assisted data dissemination methods for supporting intelligent transportation systems<sup>†</sup>
Bibliographic record
Abstract
As an essential component of Smart Cities, the intelligent transportation system (ITS) utilizes a large number of deployed traffic monitoring equipment and Internet‐of‐Vehicles technologies to timely transfer traffic management measures formulated based on accurately grasped real‐time traffic conditions to all transportation system participants for improving the operating efficiency and safety of the transportation system. The key to achieving this advantage is effective data transmission. Correspondingly, by exploiting these recorded massive traffic data, a variety of AI‐assisted data transmission methods are designed to improve the data transmission performance in the vehicular network environment (VNE) to ensure the effective operation of ITS. To help readers get an initial understanding of how AI technology can help with data transmission in VNE, in this letter, we will discuss two types of AI‐assisting methods targeting the data dissemination performance enhancement in vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communications respectively in detail, that is, predictive handover/pre‐caching algorithm and predicted traffic flow‐assisted data routing protocols. Additionally, empirical evaluation is conducted to demonstrate the effectiveness of the discussed methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".