Utilizing Autonomous Vehicles to Reduce Truck Turn Time in Ports with Application for Port of Montréal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Port congestion, particularly excessive truck turn time (TTT), disrupts supply chains, increases costs, and contributes to environmental impacts. This study evaluates the potential of integrating autonomous vehicles (AVs) into port operations to reduce TTT, using the Port of Montreal’s Viau Terminal as a case study. A discrete event simulation (DES) with agent-based logic was developed to model landside processes, including gate, yard, and staging operations, while differentiating between human-driven vehicles (HDVs) and AVs. Four scenarios were tested: Baseline indicating current operations, Truck Appointment System (TAS), partial AV integration (35% AVs) with shared resources, and AVs with dedicated staging areas and cranes. Model inputs were informed by port publicly available data and validated against observed TTT metrics. Results show that TAS reduced average TTT from 88.2 to 78.37 min; partial AV integration lowered it further to 55.91 min, with AVs averaging 45.33 min; dedicated AV infrastructure yielded the lowest AV TTT (32.86 min) but slightly increased overall TTT due to HDV delays. Findings suggest that combining AV adoption with demand management and targeted infrastructure investments can substantially improve efficiency. The study offers quantitative evidence and strategic recommendations to support port authorities in planning for automation while ensuring balanced resource allocation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it