Connected Vehicular Transportation: Data Analytics and Traffic-Dependent Networking
Bibliographic record
Abstract
With onboard operating systems becoming increasingly common in vehicles, the realtime broadband infotainment and intelligent transportation system (ITS) service applications in fast-moving vehicles become ever demanding, and they are expected to significantly improve the efficiency and safety of our daily on-road lives. The emerging ITS and vehicular applications (e.g., trip planning), however, require substantial efforts in real-time pervasive information collection and big data processing to allow quick decision making and feedback to fast-moving vehicles, which imposes significant challenges on the development of an efficient vehicular communication platform. In this article, we present TrasoNET, an integrated network framework that provides real-time intelligent transportation services to connected vehicles by exploring the data analytics and networking techniques. TrasoNET is built upon two key components. The first guides vehicles to the appropriate access networks by exploring the real-time status of local traffic, specific user preferences, service applications, and network conditions. The second mainly involves a distributed automatic access engine, which enables individual vehicles to make distributed access decisions based on recommendations, local observations, and historic information. We highlight the application of TrasoNET in a case study on real-time traffic sensing based on real traces of taxis.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".