Development of a Driving Cycle for Amman City With Performance Evaluation for ICE Vehicle
Bibliographic record
Abstract
A good driving cycle is needed for accurate evaluation of a vehicle’s performance in terms of emission and fuel consumption. Driving cycles obtained for certain cities or countries are not usually applicable to other cities or countries. Therefore, considerable research has been conducted on developing driving cycles for certain cities and regions. In this paper, a driving cycle for a taxi in Amman city, the capital of Jordan, is developed. Significant differences are noted when comparing the Amman driving cycle with other driving cycles. A model of a gasoline powered vehicle is used to conduct a performance comparison in terms of fuel economy and emissions utilizing the developed Amman driving cycle and six other worldwide driving cycles. The developed Amman driving cycle is very useful in obtaining accurate estimation of fuel economy and emissions for vehicles running on Amman roads and will be used in future work to study the performance of hybrid fuel cell/ battery vehicles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".