Rail-Truck intermodal transportation for dangerous goods
Bibliographic record
Abstract
Rail and truck are primary modes of transportation in North America. However, their use as individual modes has also raised concerns, e.g., congestion due to extensive trucking and accessibility issue of rail due to sparse rail networks. Combining these modes has the ability to overcome challenges and limitations in individual modes. As a result, rail-truck intermodal has emerged as an alternative to individual modes. A key is to efficiently configure associated activities such as inbound drayage, long haul and outbound drayage. To achieve this, there are a few studies that design intermodal networks as a hub and spoke system for regular freight. In this research, we extend the concept of intermodal hub and spoke networks to dangerous goods transportation. Our focus is primarily to address two concerns: i) make use economies of scale during long haul operation, ii) determine routings for inbound and outbound drayage activities from the perspective of risk. To this end, we develop a bi-level model in which the upper-level is a p-hub median single allocation problem to minimize the total transportation cost and the lower level is a routing problem for intermodal shipments to minimize the total transportation risk in the network. We reduce the problem to a single level and illustrate the solution on a prototype intermodal network. We demonstrate its application over an intermodal network between Alberta and Ontario through a case study.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".