An integrated modelling approach for estimating grain truck activity in the Canadian Prairie Region
Bibliographic record
Abstract
Network resiliency is foundational for the economic well-being of the agricultural industry in the Canadian Prairie Region (CPR) due to the agricultural sector relying so heavily on the rural road network. This research develops and applies an integrated modelling approach to estimate grain truck activity in the CPR. The modelling approach integrates methodologies from the truck traffic monitoring and freight demand modelling fields to establish sector-specific activity patterns. The approach consists of a 3-step commodity model, the Grain Tonnage Demand (GTD) model, which is then converted to the Hopper Bottom Truck Demand (HBTD) model using truck body type data. The results of the HBTD are then compared to those obtained using the Hopper Bottom Truck Traffic (HBTT) model, which is independently developed from truck traffic monitoring data. The comparison of the HBTD and HBTT results considers the truck kilometres travelled (TKT) by hopper bottom trucks normalized by network distance, focusing on activity in southwestern Manitoba. This research found the HBTD model to underestimate the HBTT model by 39 percent. Since neither model can be considered as ground truth, the difference should not be interpreted as an error, but rather as a way to assess the relative strengths and limitations of the different modelling approaches. For the HBTD model, these limitations relate to challenges in modelling grain activity in urban areas, the exclusion of dump trucks from the model, an inability to include all segments of the grain supply chain, trip assignment assumptions, and the limited number of commodities considered. Likewise, for the HBTT model, limitations relate to data collection approaches, sampling methods, data processing techniques, the assignment of counts to the network, and the assumption that all hopper bottom trucks carry grain. Further integration of the approaches and resolution of the limitations could yield better agreement in the future.
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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".