A Methodological Framework to Develop Optimal Sets of Driving Cycles for a Region
Bibliographic record
Abstract
RÉSUMÉ: «RÉSUMÉ:La réduction des émissions de GES provenant des transports constitue un défi de taille en raison de la demande croissante de mobilité et de la dépendance continue aux combustibles fossiles. Même si de nombreux efforts ont été déployés pour rendre les transports plus écologiques, les émissions de GES affichent une tendance à la hausse. Selon le Conseil international pour les transports propres (ICCT), l’un des principaux problèmes consiste à assurer que les émissions des véhicules soient correctement mesurées afin que les règles de contrôle de ces gaz soient respectées. Nous utilisons ce qu'on appelle le cycle de conduite pour aider à modéliser ces émissions. Il existe principalement deux types de cycles de conduite : SDC (Standard Driving Cycle) et LDC (Local Driving Cycle). Les SDC examinent les habitudes de conduite à l’échelle nationale, voire internationale, pour évaluer les émissions d’un véhicule. En revanche, les PMA se concentrent sur les habitudes de conduite dans des zones locales spécifiques, telles que les villes ou des routes particulières. En général, les PMA peuvent représenter des comportements de conduite plus réalistes. Cependant, de nombreuses études antérieures se sont appuyées sur un ou quelques cycles de conduite seulement pour représenter les comportements de conduite, ce qui peut conduire à des imprécisions dans l’estimation des émissions. Il est important de comprendre comment les gens conduisent sur tous les types de routes et dans toutes les conditions météorologiques. Des études récentes montrent que des éléments tels que la pluie, la température, la pente de la route et les limites de vitesse peuvent modifier la façon dont les gens conduisent. Ainsi, les habitudes de conduite peuvent changer d’une ville à l’autre et même en fonction de la météo.» ABSTRACT: «ABSTRACT:There is a significant challenge in reducing GHG emissions from transportation due to the rising demand for mobility and the continued reliance on fossil fuels. Even though many efforts have been made to make transportation greener, GHG emissions shows an upward trend. According to the International Council on Clean Transportation (ICCT), one big problem is making sure we measure vehicle emissions correctly so that everyone in transportation follows the rules for controlling these gases. We use something called the driving cycle to help model these emissions. There are primarily two types of driving cycles: SDC (Standard Driving Cycle) and LDC (Local Driving Cycle). SDCs examine driving habits on a national or even international scale to evaluate a vehicle’s emissions. In contrast, LDCs focus on driving habits in specific local areas, such as cities or particular roads. Generally, LDCs can depict more realistic driving behaviors. However, many past studies have relied on just one or few driving cycles to represent driving behaviors, which can lead to inaccuracies in estimating emissions. It is important to understand how people drive on all kinds of roads and in all weather conditions. Recent studies show that things like rain, temperature, the slope of the road, and speed limits can change the way people drive. So, driving habits can change across a city and even with the weather. There can be many different driving patterns in a city. If we just use one driving pattern, we lose a lot of details about how people really drive. This means we might not get a clear idea of how many harmful gases are produced by vehicles. When we talk about “information loss”, we mean that we miss out on many details about driving patterns.»
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".