A hybrid fuzzy Petri net-based approach incorporating extended grey numbers for eco-driving behaviour evaluation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Eco-driving behaviour reduces vehicle emissions, and its evaluation is key to enhancing fuel efficiency and mitigating pollution. However, existing assessment models face challenges in integrating multi-dimensional data and addressing uncertainties, which limits their accuracy and practical applicability. To address this gap, this study proposes an eco-driving evaluation framework based on Extended Grey-Number Weighted Fuzzy Petri Nets (EGWPNs). By inputting driving process data such as acceleration values and low-speed duration, the framework yields specific eco-driving scores. The framework raises a weighted extended grey-number set to unify heterogeneous data types, including discrete events like rapid acceleration frequency and continuous variables such as acceleration values. By incorporating the MYCIN confidence method for uncertainty reasoning and the Bonferroni mean operator for multi-attribute aggregation, the EGWPNs model achieves an objective assessment of driving behaviour. The framework was validated using 420, 000 real-world driving data points collected from 99 vehicles in actual driving emission experiments. The results indicate that frequent rapid acceleration exhibits the strongest negative correlation with eco-driving scores, with a weight coefficient of 0.232, followed by prolonged acceleration events and sustained low-speed acceleration, with weight coefficients of 0.112 and 0.110, respectively. Compared to traditional grey reasoning Petri nets, the EGWPNs model improves the evaluation interval shrinkage by 62.18% and demonstrates superior stability. The EGWPNs framework’s adaptability to heterogeneous data enables direct integration into intelligent transportation systems, reducing vehicular emissions through optimized traffic management and enhanced compliance with carbon neutrality policies. This study advances eco-driving methodologies while delivering scalable solutions to mitigate transportation-related environmental impacts.
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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 | 0.000 | 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 it