Can Eco-Driving Evaluation Cross Cities? Data Localization and Behavioral Heterogeneity from Beijing to Toronto
Bibliographic record
Abstract
The framework of eco-driving evaluation relying on vehicle trajectory data is to quantify the disparities of the fuel consumption for individual driving behavior and to develop a baseline under various traffic conditions. The baseline represents the typical driving behavior in a city, and it is a pivotal parameter for eco-driving evaluation. The applicability of the evaluation method in different cities is overlooked, encompassing the suitability of parameters and the minimum data required. This study aims to investigate whether the evaluation baseline developed with sufficient data can be applied to a new city. The results reveal that the baseline developed in Beijing cannot be directly transferred to the eco-driving evaluation in Toronto due to the significantly more aggressive and competitive driving behavior exhibited by Toronto drivers. This study further examines the minimum data sample size necessary to develop a robust evaluation baseline and proposes a localized method to construct the evaluation system for eco-driving evaluation in different cities.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".