k-Nearest Neighbors Queries in Time-Dependent Road Networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. In this article, we study the problem of processing k-nearest neighbors (kNN) queries in road networks considering traffic conditions, in particular the case where the speed of moving objects is time-dependent. For instance, given that the user is at a given location at certain time, the query returns the k points of interest (e.g., gas stations) that can be reached in the minimum amount of time. Previous works have proposed solutions to answer kNN queries in road networks where the moving speed in each road is constant. Obviously, these solutions cannot be simply applied to the problem we are interested in. Our approach uses the well-known A ∗ search algorithm by applying incremental network expansion and pruning unpromising vertices. We discuss the design and correctness of our algorithm and present experimental results that show the efficiency and effectiveness of our solution.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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