Evaluating Microtrip Definitions for Developing Driving Cycles
Bibliographic record
Abstract
Climate change has become one of the most critical environmental concerns of the past decades, with greenhouse gas (GHG) emissions being identified as the main culprit. Globally, policy makers have been trying to reduce GHG emissions through various policies and strategies. Given that in North America transportation accounts for 30% of total emissions, it has become the focus of attention for GHG reduction initiatives. The use of emissions models is necessary to assess the potential impact of those initiatives. The main component for emissions measurement and estimation is the driving cycle, which can be summed up as the speed profile that represents driving behaviors. The accuracy of estimations of emissions strongly depends on the accuracy of the driving cycles used; using inaccurate driving cycles would not be representative of real-world driving patterns and could provide erroneous results, even if the model used were the most reliable possible. Driving-cycle development has different steps, one being to divide the speed profiles into smaller sections called microtrips. There are several methods for establishing the parameters of the microtrips created; in this study, such methods, as well as a new one based on distance, were compared to determine which method could result in the most accurate driving cycle. The results show that microtrips based on spatial characteristics provide more representative driving cycles, whereas among spatial characteristics, distance-based approaches resulted in the most accurate driving cycle.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".