Port Access Fluidity Management during a Major Extension Project: A Simulation-Based Case Study
Bibliographic record
Abstract
The increasing demand for freight services and the use of larger vessels to meet this demand has led to challenges related to storage space and logistics activities, highlighting the need for improvements in port infrastructure for better logistics management. At a crucial phase in its growth, the Port of Trois-Rivières in Canada is planning a major expansion, including the construction of a new terminal to enhance its hosting capacities and freight services. This expansion faces potential access congestion problems during the planned construction, exacerbated by the port’s urban setting. In response to the needs identified by the port authorities for this event, the study’s objective is to assess the implications of increased construction and freight truck flows on access gate fluidity and the impact of additional access infrastructure investment to mitigate potential congestion. These evaluations aim to define effective access management strategies throughout the construction period of the new terminal. To address these complexities, our approach is based on scenario analysis in variants co-constructed with the partner. These scenarios are evaluated using simulation models, configured according to parameters calibrated with a granularity that allows congestion detection. The results enabled an evaluation of the capability of existing and potential gates to manage access. Subsequently, recommendations were shaped in accordance with the expected objectives to manage access traffic effectively. These recommendations concern the optimization of construction activity planning, the layout and planning of access, and the importance of enhanced collaboration between municipal and port authorities for more controlled road traffic management. Recognizing the importance of synchromodality, road network centrality management, and the outsourcing of capacity through inter-port cooperation and with dry ports to manage congestion, these tools will be discussed in this work. The study proposes an approach that reconciles scientific rigor with the implementation constraints of the proposed solutions, allowing this study wider applicability in various port contexts facing challenges in this field of study.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".