Hidden Markov model and driver path preference for floating car trajectory map matching
Why this work is in the frame
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Bibliographic record
Abstract
Here, a hidden Markov model (HMM) and driver path preference (DPP)‐based algorithm was proposed for floating car trajectory map matching. The algorithm focused on two improvements over existing HMM‐based map matching algorithm: (i) the use of distance difference feature and average speed difference feature for transition probability calculation, which reasonably describe the context information between the two adjacent sampling points. It results in a more accurate matching capability; (ii) the DPP overcomes the shortcoming of feature attenuation in calculating the transition probability at low floating car sampling rates. It assures the matching accuracy of the algorithm at low sampling rates. The algorithm was evaluated using ground truth data and the results of the experiment show that the new transition probability significantly improves the matching capability. The proposed DPP can significantly help to maintain the matching accuracy under the condition of low sampling rates.
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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