Information Technology and Efficiency in Trucking
Bibliographic record
Abstract
Abstract. We develop an econometric model to estimate the impact of Electronic Vehicle Management Systems (EVMS) on the load factor (LF) of heavy trucks. This technology is supposed to improve capacity utilization. The model is estimated on the Quebec subsample of the 1999 National Roadside Survey. The LF is explained as a function of truck, trip, and carrier characteristics. We show that the use of EVMS results in an increase of 16 percentage points of LF on backhaul trips. However, we also find that there is a rebound effect on fronthaul movements, with a reduction of LF by about 7.6 percentage points. JEL classification: O33, Q55 Technologie de l’information et efficacite ́ dans le camionnage. Nous estimons un modèle économétrique pour évaluer l’impact des systèmes de gestion électronique des véhicules (SGEV) sur le taux de chargement (TC) des camions lourds. Cette technologie est censée améliorer l’utilisation de la capacité. Le modèle est estime ́ sur le sous-échantillon québécois des données de l’enquête nationale routière en bord de route de 1999. Le TC est expliqué en fonction des caractéristiques du camion, du voyage et de l’entreprise de transport. Nous montrons que l’utilisation de SGEV accroit le TC sur le retour d’environ 16 points de pourcentage. Par contre, nous trouvons également un effet rebond sur l’aller avec une réduction de TC d’environ 7.6 points de pourcentage. 1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".