A Safety Analysis of Left-Turning Maneuvers for Long-Combination Vehicles
Bibliographic record
Abstract
A growing number of goods are shipped in Ontario using long-combination vehicles (LCVs). LCVs can significantly increase the amount of cargo carried per shipment, while reducing shipping costs and environmental impacts of freight road travel. Questions remain regarding their safety due to their larger size and wider maneuvering. This thesis furthers our understanding of LCV safety considerations in last-mile areas during left-turning maneuvers by focusing on conflicts with infrastructure and road users. Intersections are selected along Peel Region’s Strategic Goods Movement Network and left-turning swept path analyses are performed using AutoTurn. A microsimulation of the intersections is developed, where the existing network is modeled in Vissim while LCVs are added as potential road users. The Surrogate Safety Assessment Model is then used to analyze the microsimulation results pertaining to potential collisions. It was found that existing last-mile infrastructure is ill-equipped to accommodate LCVs and requires adjustments for future LCV use.
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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 | 0.001 |
| 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) | 0.003 | 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".