Comparison study on the urban transportation fuel consumption and GHG emission using real-world vs. MOBILE6 and MOVES estimations for gasoline and hybrid electric vehicles
Bibliographic record
Abstract
This work presents a methodology to compare vehicle fuel consumption and GHG emissions from real-world in-use testing with EPA MOBILE 6 and MOVES (MOtor Vehicle Emission Simulator) estimations. For this study, fuel consumption data in real-world driving conditions from a sample of 74 instrumented vehicles is used, 21 of which are HEVs. Fuel consumption from the vehicles during the testing were recorded, analyzed, and compared to estimated emissions using the current EPA emissions estimation model, MOtor Vehicle Emission Simulator (MOVES) and MOBILE6. The authors observed discrepancies between the measured data and these estimates, especially when associated with cold-start emissions. More detailed analysis results, along with the detailed test methodologies, are provided in this paper. Among other results, the beneficial fuel efficiency merits of hybrid vehicles are demonstrated in particular in low speeds in urban (city) driving conditions. There is discrepancies between MOVES and MOBILE6, and real-world estimations in lower speeds. At speeds lower than 20km/hr the fuel consumption curves of the two former methods are slightly higher than the latter. However the former mentioned methods don’t consider cold-start emissions. The average GHG emission obtained from MOVES and MOBILE6 are slightly higher than estimations based on real-world fuel consumption curves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".