Performance of a New Enhanced Topological Decision-Rule Map-Matching Algorithm for Transportation Applications
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Bibliographic record
Abstract
Map-matching problems arise in numerous transportation-related applications when spatial data is collected usinginaccurate GPS technology and integrated with a flawed digital roadway map in a GIS environment. This paperpresents a new enhanced post-processing topological decision-rule map-matching algorithm in order to addressrelevant special cases that occur in the spatial mismatch resolution. The proposed map-matching algorithm includessimple algorithmic improvements: dynamic buffer that varies its size to snap GPS data points to at least one roadwaycenterline; a comparison between vehicle heading measurements and associated roadway centerline direction; and anew design of the sequence of steps in the algorithm architecture. The original and new versions of the algorithmwere tested on different spatial data qualities collected in Canada and United States. Although both versionssatisfactorily resolve complex spatial ambiguities, the comparative and statistical analysis indicates that the newalgorithm with the simple algorithmic improvements outperformed the original version of the map-matching algorithm.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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