Development of a driving cycle for Colombo, Sri Lanka: an economical approach for developing countries
Why this work is in the frame
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Bibliographic record
Abstract
Summary Financial constraints and lack of availability of traffic‐related information significantly hinder the development of driving cycles in developing countries. This paper proposes an economical, practical, accurate methodology for the development of driving cycles, including the development of a driving cycle for Colombo, Sri Lanka. The proposed methodology captures regional traffic and road conditions and selects a model that represents the collected data sample with minimum available traffic‐related information. Existing methods were modified for route selection by dividing routes into links using nodes or physical junctions to minimize the number of trips required for data collection. Speed–time data for respective links were used to reconstruct speed–time profiles of identified origin–destination pairs. The on‐board method was used for data collection, and the Markov chain theory was used to develop a transition probability matrix of state changes. An additional matrix was introduced to the existing method to improve model representativeness to the collected data sample. Copyright © 2016 John Wiley & Sons, Ltd.
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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