Representative driving cycle construction incorporating road grade transitions using a Markov-chain method
Bibliographic record
Abstract
Driving cycles are needed for vehicle design, fueleconomy analysis, and transportation emission estimation. Despitetheir significant role, conventional driving cycle construction methodsoften fail to capture the full range of real-world driving dynamics,primarily due to their limited consideration of road grade. In thiswork, a Markov Chain-Based (MCB) methodology for constructingrepresentative driving cycles is presented, which integrates extensivereal-world data, including vehicle speed, acceleration, and road grade.By leveraging a sparse transition matrix, our proposed approachenhances computational efficiency and is scalable to large statespaces. Experimental evaluations demonstrate that incorporating roadgrade significantly improves driving cycle representativeness, withthe mean Vehicle Specific Power (VSP) changing from 1.45 to1.44 kW/tonne (a 0.69% decrease), variance increasing from 4.29to 6.15 (a 43.3% increase), and the maximum VSP rising from7.53 to 11.6 kW/tonne (a 54.2% increase). Quantitative assessmentsfurther demonstrate that while average speed and acceleration errorsare maintained within 8.31% and 6.03%, respectively, idling time isunderestimated by 68.7% compared to the experimental data, which isa potential area for future refinement. Overall, the results underscorethat the representative driving cycle incorporating vehicle speed,acceleration, and road grade provides a better foundation for accurateperformance evaluations and emissions analyses. Future research willfocus on further optimizing computational efficiency and extendingthe framework to account for additional variables such as weatherconditions and cold climate effects, helping to contribute to theadvancement of next-generation, eco-friendly transportation systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".