Exposure Modelling of Productivity-Permitted General Freight Trucking on Uncongested Highways
Bibliographic record
Abstract
The research designs, develops, validates, and applies an exposure model of productivity-permitted general freight trucking on uncongested highways. Productivity-permitted general freight trucks (long trucks) are multiple trailer configurations, consisting of van trailers, which exceed basic vehicle length limits but operate within basic weight restrictions. The three predominant long trucks in North America are Rocky Mountain doubles (Rockies), Turnpike doubles (Turnpikes), and triple trailer combinations (triples). Long trucks have been used in Canada since the late 1960s. Recent highway investments in the Canadian Prairie Region have effectively completed the network on which long trucks are allowed to operate. Despite widespread use of long trucks for many years and these recent infrastructure investments, there is a knowledge deficiency about long truck exposure. The research uses the transportation systems analysis approach to design, develop, and validate the long truck exposure model. Exposure is expressed as an explanatory variable in three principal dimensions (volume, weight, and cube), which is needed for predicting transportation system impacts of long truck operations. The research applies the model to clarify issues that should be considered in establishing charges for long truck permits, determining long truck safety performance, and developing load spectra for long trucks. The exposure model relies on a unique dataset that integrates output from a classification algorithm, field observations, and industry intelligence. The results indicate that long trucks travelled 67 million kilometres on a 10,000 centreline-kilometre highway network in the Canadian Prairie Region in 2006. The model demonstrates strong temporal and geographic concentration of long truck travel on the network. Application of the results reveals the following findings: • Decisions about establishing long truck permit charges are supported by consideration of options within a revenue adequacy rationale that are sensitive to freight density and the distance travelled by long trucks. • The exposure-based collision rate for Turnpikes is half of the collision rate for Rockies, about one-third of the rate for legal-length articulated trucks, and one-quarter of the rate for triples. • The model provides loading indicators required for pavement and bridge design and evaluation procedures and demonstrates the cubic orientation of long truck operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".