Urban Commercial Vehicle Movement Model for Calgary, Alberta, Canada
Bibliographic record
Abstract
Commercial vehicle movements compose perhaps 15% of all urban vehicle trips and produce large impacts in key areas, such as congestion, emissions, road wear, and industrial area traffic. A system for modeling such movements was developed for Calgary, Alberta, Canada. It is a novel application of an agent-based microsimulation framework that uses a tour-based approach and emphasizes important elements of urban commercial movement, including the role of service delivery, light commercial vehicles, and trip chaining. The microsimulation uses Monte Carlo techniques to assign tour purpose, vehicle type, next-stop purpose, next-stop location, and next-stop duration. Tours are “grown” with a return-to-establishment alternative within the next-stop purpose allocation, which is consistent with the nature of tour making in urban commercial movements. The Monte Carlo probabilities are established with the use of a series of logit models, with coefficients estimated on the basis of observed behavior of different commercial movement segments. The estimation results in themselves provide insights into the revealed behavior that have not been available previously.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".