Long-Term Traffic Forecast Using Neural Network and Seasonal Autoregressive Integrated Moving Average: Case of a Container Port
Bibliographic record
Abstract
Long-term insight into maritime traffic is critical for port authorities, logistics companies, and port operators to proactively formulate suitable policies, develop strategic plans, allocate budget, and preserve and improve competitiveness. Forecasting freight rate is a spotlight in port traffic literature, but relatively little research has been directed at forecasting long-term vessel traffic trends. Based on forecast long-term freight rate input provided by the recent 10-year strategic planning of the port of Rajaee, the largest port of Iran, the paper implements seasonal autoregressive integrated moving average (SARIMA) and neural network (NN) models to forecast its container vessel traffic between 2020 and 2025. A database consisting of monthly container traffic data for this port from 1999 to 2019 is utilized. The comparison between the two forecasting models is fulfilled by benchmarking the naïve method. The results reveal the superiority of the NN model over SARIMA in this practice. Considering NN model outputs, the port should expect a significant increase in Panamax and Over-Panamax vessels in the future, and, if not timely addressed, this would result in a systemic queue in the port of Rajaee. That said, the approach can be implemented in port planning and design to avoid under- or over-estimations in such capital-intensive projects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".