Development of On-Road Exhaust Emission and Fuel Consumption Models for Motorcycles and Application through Traffic Microsimulation
Bibliographic record
Abstract
This study developed on-road exhaust emission and fuel consumption models for application in traffic microsimulations to estimate motorcycle emissions and fuel consumption in an Asian developing city. The motorcycle onboard measurement system was developed to instantaneously measure and continuously record on-road driving data, including the speed-time profile, exhaust emissions, and fuel consumption per second. The test motorcycle was driven on roads around Khon Kaen City, Thailand, to collect on-road driving data during the morning peak hours for a total of 112 hours. The collected on-road driving data were applied to develop on-road exhaust emission and fuel consumption models using regression analysis. The models were developed with high correlations among the amount of exhaust emissions and fuel consumption and the instantaneous speed and acceleration rate. The developed models were applied with a traffic microsimulation to evaluate the exclusive zone for motorcycles stopping at a signalized intersection. The evaluation results reveal that it could improve the level of intersection service by decreasing travel times, delays, and queue lengths at intersections, as well as by reducing the fuel consumption and emissions of vehicles travelling through intersections compared with these values under the existing conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".