Algorithm for Detecting Outliers in Bluetooth Data in Real Time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bluetooth detectors are becoming increasingly popular as a technology for acquiring travel time data. However, these data include outliers that are caused by several factors, and a robust outlier detection algorithm is needed for filtering out the outliers. Arterial roadways present a particularly challenging environment because the traffic control devices introduce a large amount of variability to the measured individual travel times and because of the prevalence of other sources of error (e.g., en route stops, Bluetooth-enabled devices not in vehicles). This paper presents a new adaptive outlier detection algorithm that is proactive rather than reactive. Unlike conventional reactive algorithms that rely solely on recent data, the proposed algorithm uses both historical data and current data to predict the validity window. The performance characteristics of the proposed algorithm are illustrated, and field data from a signalized arterial are used to compare the proposed algorithm and a benchmark algorithm. The results show that the proposed model is superior to the benchmark model and that the model performs well across a wide range of traffic conditions.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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