Smoothing of vehicular trajectories under heterogeneous traffic conditions to extract microscopic data
Bibliographic record
Abstract
Trajectory data collected using video image processing techniques are prone to noise. Trajectory data extracted using commercially available video image processing software (TRAZER) contains the noise associated with the false detection in addition to the white noise. This paper proposes a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to smooth such trajectory data. In this approach, trajectory data are decomposed into a finite number of intrinsic modes and a unique residue is computed to obtain each mode. This monotonic residue gives the smoothed trajectory. The instantaneous speeds of the vehicles are then estimated using the method of continuous wavelet transforms, discrete wavelet transforms, and numerical differentiation. Internal consistency analyses show that the wavelet transforms methods are effective in reducing the noise amplification of the speed profile. It was also observed that the corrections applied on trajectory data have a significant effect on macroscopic traffic relations.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".