Studying the impact of DSRC penetration rate on lane changing advisory application
Bibliographic record
Abstract
Vehicular communication technology leverages communication equipment and infrastructure to improve road safety and provide useful services for road user. In order to operate properly, many of these services need continuous data gathering to assess road situations accurately. In this paper, we analyse the impact of communication technology penetration rate and the proportion of application-equipped vehicles on the efficiency of a Lane Changing Advisory Application in improving travel delay and traffic fluidity. We model the system analytically and perform extensive simulations at different penetration rates both with a microscopic traffic simulator and a network simulator. Vehicle-to-vehicle communication outcomes influence application efficiency and therefore driver behaviour. The mobility pattern is fed back into the traffic simulator in a closed loop in order to assess traffic fluidity. The results of the study present the impact of DSRC penetration ratio on the application performance and by consequence on road traffic fluidity. They show that even at low penetration rates of 10% and 25%, total travel time and traffic fluidity are enhanced.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".