Vehicle-Specific Virtual Traffic Control Strategy to Reduce the Start-Up Delay for Autonomous Heavy Trucks
Bibliographic record
Abstract
Heavy trucks, with their slow acceleration and reduced speed, can cause traffic delays at intersections, thereby affecting the efficiency of traffic flow. To address this issue, this paper introduces a virtual traffic control system called advanced stop point and prior start time (ASP-PST). The ASP-PST leverages vehicle-to-infrastructure (V2I) communications between traffic signals and connected and automated heavy trucks. The system guides autonomous heavy trucks on where to stop and when to initiate their movement ahead of the green light. This strategy reduces start-up delays by managing vehicle movement control, enabling trucks to reach sufficient speed before approaching the intersection, thereby ensuring smooth passing and harmonized traffic flow. An analytical solution for the ASP-PST has been developed and validated through microscopic simulation tests at signalized intersections. The results show reductions in travel time of up to 50% in mixed traffic conditions, consisting of both heavy trucks and general cars, and illustrate the formation of well-coordinated platoons with uniform spacing. Demonstrating efficacy across various network environments, the ASP-PST offers a potential solution to enhance traffic flow and reduce congestion caused by heavy trucks at intersections.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".