The Trend Toward Use of Smaller Trucks: Modeling Historical Urban Truck Movements
Bibliographic record
Abstract
This study investigates how truck flow characteristics have changed over time in the Region of Peel, a region just west of Toronto that is considered to be a manufacturing, warehousing and goods movement/logistics hub for the Greater Toronto Area. Various economic indicators are investigated to determine which are most closely related to truck volumes and multivariate regression models are then developed to predict heavy and medium trucks on arterial roads and freeways. These models are used to help explain some remarkable recent trends in truck movements. In addition to substantial rates of growth in non-recessionary periods from 1981 to 2004, there has been a notable shift from heavy to medium trucks in the last 5 years. This study has tested, but has found no evidence that this is due to congestion, but rather that it can be at least partially explained by the increased urbanization of the Region of Peel and increasing diesel fuel prices. A series of multivariate models have been developed for heavy and medium trucks on freeways and arterial roads. The most credible set of models tested are functions of the regional population, the unemployment rate, the value of international exports from the province of Ontario, and the real price of diesel fuel. While these models do not completely explain the dramatic shift from heavy to medium trucks in the period from 2001 to 2004, the models do predict a more gradual, sustained shift from heavy to medium trucks as the population of the region continues to grow. This analysis is considered to be a useful benchmark forecast that can be used to augment more spatially detailed modeling efforts for the Greater Toronto Area.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".