Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle
Why this work is in the frame
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Bibliographic record
Abstract
The challenges in the development of plug-in electric vehicle (PEV) powertrains are efficient energy management and optimum energy storage, for which the role of driving cycles that represent driver behaviour is instrumental. Discrepancies between standard driving cycles and real driving behaviour stem from insufficient data collection, inaccurate cycle construction methodology, and variations because of geography. In this study, we tackle the first issue by using the collected data from real-world driving of a fleet of 76 cars for more than one year in the city of Winnipeg (Canada), representing more than 44 million data points. The second issue is addressed by a proposed novel stochastic driving cycle construction method. The third issue limits the results to mainly Winnipeg and cities that have similar features, but the methodology can be used anywhere. The methodology develops the driving cycle using snippets extracted from recorded time-stamped speed of the vehicles from the collected database. The proposed Winnipeg Driving Cycle (WPG01) characteristics are compared to eight existing standard driving cycles and are more able to represent aggressive driving, which is critical in PEV design. An attempt is made to isolate how many differences could be attributed to the sample size and the methodology. The proposed construction methodology is flexible to be optimized for any selection of driving parameters and thus can be a recommended approach to develop driving cycles for any drive train topology, including internal combustion engine vehicles, hybrid vehicles, plug-in hybrid, and battery electric vehicles. Characterization of vehicle parking durations and types of parking (home, work, shopping), critical for duty cycles for PEV powertrains, are reported elsewhere. Here, the focus is on the mathematical approach to develop a drive cycle when a large database with high resolution of driving data is available.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it