An evolutionary framework for estimating turning movements at road intersections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Turning movements are one of the key inputs required for several traffic studies. Several methods have been developed to measure them. However, present techniques have high operational or capital costs, which motivate researchers to develop new techniques to estimate turning movements. However, there is neither a flexible technique available to make best use of different available information types, nor a framework that supports deciding additional data to achieve a target accuracy. This paper proposes a new methodology using all available data to identify the subspace containing all solutions and determine its centroid; thus, providing the most realistic and non-extreme solution. In addition, a framework, including scenarios with different data combinations, is developed with capability to evaluate the proposed solution and then locate further measurements to achieve the target accuracy. The framework is validated using a considerable set of intersections at Edmonton city, Canada. The results show that the proposed framework can achieve the target accuracy with minimum field measurements saving time, effort and cost.
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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