Investigating the impacts of connected vehicle technology on the flow of trucks at the busiest Canada-U.S. border crossings
Bibliographic record
Abstract
• Truck traffic at international land border crossings is not well studied. • The Canada-U.S. land border crossing is one of the busiest in the world. • Vehicle-to-vehicle and Vehicle-to-infrastructure scenarios are simulated. • A DTA model is used to simulate the movement of trucks from Canada to the U.S. • V2V and V2I will improve the performance of truck traffic flows at the border. Land-border crossings between Canada and the United States facilitate the movement of approximately 59 % of the goods traded between the two countries. Consequently, these border facilities experience heavy truck traffic daily. While connected vehicle technology have attracted attention in recent years, there has been no attempts to assess its impacts on truck traffic performance at international land borders. This paper addresses the issue by developing and applying a microscopic traffic simulation model for connected trucks. Scenarios depicting the movement of trucks between Canada and the U.S. through the two busiest border crossings (i.e., Windsor and Sarnia), are simulated in the presence of V2V and V2I technologies with the help of a dynamic traffic assignment. The simulation results suggest that truck traffic becomes more streamlined with up to 7 % of all trucks switching to the Sarnia crossing under a 100 % V2V scenario when a delay incident is present on the corridor leading to Windsor. Also, average time delay at the Windsor crossing under extended delay conditions spanning over a course of 8 h at this crossing is reduced by 30 % (i.e., delay dropped from 5 h to 3.5 h) when V2I technology is utilized.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".