Bibliographic record
Abstract
In the transition step to the near future where autonomous vehicles fill the highways, the autonomous vehicles’ successful implementation counts on knowledge about their interaction with conventional vehicles. Due to the lack of numbers of the autonomous vehicles on roadways, many transportation professionals depend on simulations in order to examine the coexistence of both vehicle types and their interaction in the circumstance of higher market penetration rates of the autonomous vehicles. In this study, VISSIM microscopic simulator is used for inspecting the autonomous vehicles interactions and assessing their impacts on traffic stream. A case study that evaluates the effects on vehicles throughput, delay, queue length, and safety at the highway work zone merging area is investigated. The simulation was generated the proximity of the Louis-Hippolyte La Fontaine tunnel, which connects Boucherville and Montréal island. To simulate coexist periods, the autonomous vehicles were put into the simulation with different penetration rates starting at 20% and increasing 20% for each scenario until reaching 80% of the rates of the autonomous vehicles. Furthermore, the safety impact of the autonomous vehicles in the matter of conflicts was studied using the Surrogate Safety Assessment Model (SSAM). The simulation results showed that the tunnel work zone’s capacity per lane was increased when CAVs were added to the simulation. The average vehicle delay did not improve a low CAV penetration rates. However, as CAVs account for more than 40% of the total passenger vehicles, the vehicle delay improved. The simulated model also showed that the average queue length increased with the increase of CAV in the traffic stream. Nonetheless, the conflict analysis results proved that CAVs can improve overall traffic safety at the work zone.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".