Safety Evaluation of Truck Lane Restriction Strategies Using Microsimulation Modeling
Bibliographic record
Abstract
The demand for goods and services in North America has increased dramatically over the past few decades. This demand increase resulted in an associated increase in truck traffic on North American highways, escalating congestion, operation, and safety concerns. This in turn has led to increasing interest in strategies to reduce interaction between trucks and cars, including truck restrictions and dedicated truck lanes. The purpose of this paper is to implement algorithms for evaluating safety measures for different scenarios of truck lane restrictions and dedicated truck lanes using microscopic traffic simulation. The measures of performance calculated in this study are lane changing, merging, and rear-end conflicts. These measures are analyzed for truck-restricted lanes and dedicated truck lanes on the Gardiner Expressway in downtown Toronto, Canada. Simulation scenarios are developed by varying lane strategies and truck percentage. Simulation results showed that implementation of a single truck-restricted lane makes little difference to most conflict measures. Restricting trucks from the leftmost two lanes results in more substantial reductions in lane changing conflicts, but causes some increased freeway merging conflicts involving trucks. Dedicating the leftmost lane only to trucks also reduces lane changing conflicts but increases lane merging conflicts. Because lane changing conflicts are far more frequent than merging conflicts, there appears to be a net safety benefit by either restricting trucks from the left two lanes or dedicating the left lane to trucks. Relationships between lane strategy and rear-end conflicts are weak. Truck lane strategies are most effective when truck percentage exceeds 15%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".