Freight distribution analysis and modelling of inland waterway transport using big data
Bibliographic record
Abstract
The Yangtze River economic belt (YREB) serves as the main east–west axis of China to promote economic development and environmental protection along the Yangtze River (YR). The factors that affect the freight distribution of major types of cargo transported through the YR were analysed using automatic identification system data and ship visa data. First, a set of freight impedance functions was developed for different types of links of the waterway network, considering a number of factors (e.g. cargo type, delays at ship locks, water levels and flows at different waterway segments and upstream and downstream shipping speeds). Distance-based (DB) and time-based (TB) impedance matrices of different types of cargo were computed. A gravity model (GM) and an intervening opportunity model were then used to simulate the distributions of different types of cargo based on the computed impedance matrices. A trip length distribution method was applied to validate the estimated distribution models. The results showed that the GM with a power term outperformed the other models and that the TB models were superior to the DB models for the prediction of freight distributions over large geographies like the YREB. This work offers an in-depth understanding of the freight characteristics of inland waterways and therefore should be helpful for relevant authorities in formulating port and inland waterway plans and policies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".