An Econometric Analysis of the Impact of Electronic Vehicle Management Systems on the Load Factor of Trucks Operating in Quebec
Bibliographic record
Abstract
This paper develops an econometric model that highlights the main factors affecting trucks load factor (LF). More specifically, the paper assesses the impacts associated with electronic vehicle management systems (EVMS) that are supposed to increase LF by reducing coordination costs between demand and supply. The model is estimated on a subsample of the 1999 National Roadside Survey covering heavy trucks traveling in the province of Quebec. The LF is explained as a function of vehicle configuration, type of trailer, type of trip, and the nature of carrier operations. The paper shows that the use of EVMS results in an increase of LF between 5 and 10 percentage points on backhaul trips while it slightly lowers LF on front haul movements. This last effect could represent a sort of rebound effect. The paper also shows that the overall impact of this technology on the industry energy efficiency was relatively limited in 1999 because a low adoption rate.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".