Modeling impacts of freight automated vehicles in the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
Automated vehicle (AV) technology will bring about disruptive changes to transportation of people and goods. To better understand such changes through macroscopic modeling, studies so far predominantly focused on passenger AVs. This paper examines the impacts of freight AVs at the network level in the Greater Toronto and Hamilton Area (GTHA), Canada. This is achieved by implementing several automation scenarios in a recently developed commercial vehicle model for the GTHA. The scenarios are designed based on some of the anticipated changes in vehicle technology and the freight logistics market. They represent a range of possibilities that may occur at partial and full market adoption of freight AVs. Model results suggest that network-level congestion will increase in the near term when automated trucks operate alongside human-driven trucks and are allowed on the freeways only. However, with full market adoption, the network will be less congested compared to no automation, even if “induced” truck demand is accounted for. The overall truck vehicle kilometers travelled (VKT) will increase during partial and full truck automation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".