Temporal, physical, and spatial distribution characteristics of urban container trucks versus other articulated trucks
Bibliographic record
Abstract
Container traffic and the associated interest in developing inland ports to attract this traffic are growing in metropolitan areas. This traffic typically requires drayage trucks to transport containers between intermodal terminals and urban shippers and to hinterlands beyond the urban network, also known as the “last mile.” Container trucks are different than other trucks and their movements are straining the capacity and operation of transportation facilities. Therefore transportation engineers and planners must explicitly consider container trucks in their designs. This paper quantifies the temporal, physical, and spatial distribution characteristics of container trucks and reveals differences between other urban articulated trucks in Winnipeg, Manitoba, Canada. This research finds that total and articulated truck traffic data are poor surrogates for container truck traffic data and do not represent container truck characteristics. Peak container truck volumes occur during different times of the day than other articulated trucks and total traffic; corridors with high truck volumes do not necessarily have high container truck volumes, and vice versa; and about 80 percent of container trucks have tridem axles with the remaining having tandem axles whereas about 20 percent of articulated trucks have tridem axles with the remaining having tandem axles.
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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) | 0.001 | 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".