Quantifying the Criticality of Highway Infrastructure for Freight Transportation
Bibliographic record
Abstract
Events that disable parts of the highway transportation network, ranging from weather conditions to construction closures, may affect freight travel times and ultimately degrade economic productivity. Although previous studies of criticality typically focused on the impacts of natural disasters or terrorist attacks on systemwide travel times, these studies did not quantify the costs associated with disruptions to the economy because of disruptions to the freight transportation system. This paper quantifies the economic criticality of the highway infrastructure in Ontario, Canada, with the use of a new measure of criticality that determines the cost of highway closures (in dollars) on the basis of the value of goods, the time delayed, and the associated value of time. When criticality is measured in this way, it has some correlation with truck volumes, but the correlation differs when the values of shipments and the physical redundancy in the network are considered, and results in new insights into critical freight infrastructure. For example, the highway network within the greater Toronto, Ontario, Canada, area has a high degree of redundancy, but highways farther away from this metropolitan area have less redundancy and are thus more critical. Moreover, sections of Highway 401 located west of the greater Toronto area were found to be more critical—even though it carries lower truck volumes—than those located east of the greater Toronto area because of the lower redundancy in the western portion of the network. This measure has many potential applications in freight transportation planning, operations, and maintenance. Finally, with the cost of these disruptions quantified in dollars, one can then calculate the monetary benefits of potential transportation improvements for comparison (i.e., perform a cost–benefit analysis).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".