A simulation-based benefit analysis of deploying connected vehicles using dedicated short range communication
Bibliographic record
Abstract
In this research we utilize PARAMICS traffic micro-simulation software to study the impact of deploying Connected Vehicles (CV) in Deerfoot trail, Calgary, Alberta. We have implemented a V2V (Vehicle-to-Vehicle) Assisted V2I (Vehicle-to-Infrastructure) system for PARAMICS. It uses Dedicated Short Range Communication (DSRC) protocol to acquire traffic data, calculate and compare important traffic safety and mobility parameters and their impacts on CV by testing five scenarios differentiated by the percentage of 0% to 40% market penetration of CVs. Despite of previous studies which focused on upstream traffic, in this study we demonstrate effect of considering DSRC, re-routing guidance and advisory speed for upstream and downstream traffic. The study demonstrated that the CV technology can enhance traffic safety and mobility in freeways, if the percentage of CVs is significant (e.g. 30–40%) and the CV technology is accompanied by advisory speed reflected on Variable Message Signs (VMS) on both upstream and downstream of the incident location using DSRC range. In other words, equipping freeways with VMS, to use V2I communication, complements the CV technology, improves CV efficiency and leads to higher safety and mobility enhancement in freeways.
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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 | 0.001 |
| 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".