Safety and Operational Impact of Truck Platooning on Geometric Design Parameters
Bibliographic record
Abstract
The most well-known benefits of heavy commercial vehicle (HCV) platooning are fuel savings and emission reductions. HCV platooning under SAE automation level 4 or 5 would also address the truck driver shortage by eliminating the driver from one or more HCVs in a platoon. This dissertation investigates the safety and operational implications of SAE level 4 HCV platooning on North American roadways. The research develops modified analytical models and micro-simulation models (PTV VISSIM) for analyzing impacts on two-lane rural highways, urban arterial roadways, and freeways. The study considers different time headways (0.6 sec and 1.2 sec) between the platooning vehicles, and three market penetration rates (0%, 5%, and 10%). The two-lane rural highways chapter investigates the passing sight distance (PSD) required to overtake an HCV platoon. The urban arterial roadways chapter compares existing traffic controls with traffic signal priority (TSP) for HCV platoons. The freeways chapter investigates freeway acceleration lane length on merging segments for HCV platooning operations. The findings suggest that two-HCV platooning with 0.6 sec time headway and a 5% market penetration rate can be allowed on designated North American roadways. With proper passing lanes, two-HCV platoons can be operated on two-lane rural highways that already permit long combination vehicle operations. Even with TSP, HCV platooning on urban arterial roadways at penetration rates higher than 5% at our selected intersection may, however, cause significant delays and overwhelm the traffic system. On freeways, two-HCV platooning at a 5% market penetration rate where the freeway acceleration lane is at least 600m long appear to be feasible. The study will assist transportation professionals and policymakers in understanding the consequences of HCV platoons and deciding whether to allow HCV platooning on North American roadways.
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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) | 0.000 | 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) | 0.003 | 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".