Utilizing Autonomous Vehicles to Reduce Truck Turn Time in Ports with Application for Port of Montréal
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".