Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data
Bibliographic record
Abstract
The location data from positioning devices such as those utilising global navigation satellite system (GNSS) provides vital information for the probe‐car systems aiming at solving road‐traffic problems. In the case of the Japanese Electronic Toll Collection System 2.0, a huge amount of probe data can be gathered at intervals of 200 m throughout the country. However, it is not easy for conventional map‐matching algorithms to perform appropriately when they target this sparse probe data. Since for the sparser probe data of this range, it is required to check the reachability of the probe car between adjacent position fixes by using the Dijkstra's algorithm or A* algorithms. These algorithms, however, consume much computation power and can be a serious obstacle for map‐matching processing, especially in real‐time applications. The authors propose a new dynamic‐programming‐based map‐matching algorithm, which can also reduce the calculation time for the reachability test by introducing a hash algorithm. The results of the evaluation confirm the robustness and the effectiveness of the proposed algorithm in terms of both accuracy and computational performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".