Container transportation service demand simulation model for United States coastal container ports
Bibliographic record
Abstract
Investment in container ports and associated multimodal facilities raise difficult economic issues and present major societal challenges. Attempts to resolve these issues require development of new methods and extensions of existing approaches drawing upon advances in simulation techniques and their integration with economic theory. Demand for container port services is a critical factor for the economic analysis of, and decision making for, container port development. However, practical methods for demand estimation often are not convincing, while formal research in transportation demand estimation has not been extended to the practical use of container port demand estimation. This dissertation designed, developed, and implemented a multimodal container transportation simulation model. It takes as given, the quantity of international containerized trade and the existing multimodal transportation system, and assumes the containers be transported along a least-cost route from source to market. The cost includes the fees paid to the transportation services, and the inventory cost. The estimated demand separates the effect of the supply side, and measures only the substitution effect of the existing ports. The simulation efforts in this research included the national and state highway system, and the railway system. It includes 14 existing major US ports, with 6 in East Coast, 4 in West Coast and 4 in Gulf Coast. Foreign countries are grouped into continents, with Asia divided into West Asia and East Asia by Singapore. The developed software was applied to the demand analysis for a hypothetical port at Quonset Point, Rhode Island, USA. The application includes the estimated demand under existing conditions, demand changes with rail improvement, and with competition by the eastern Canadian ports of Halifax and Montreal. This dissertation contributes to the container port demand estimation literature, and provides a new tool for decision makers and business operators concerned with multimodal container transportation facility development.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".