Probabilistic Data-Driven Approach for Real-Time Screening of Freeway Traffic Data
Bibliographic record
Abstract
Freeway traffic surveillance systems currently collect large amounts of traffic data, sometimes a few gigabytes per day, to support various critical traffic management center functions such as incident detection, travel time and delay estimation, and congestion management. Reliable traffic information, however, requires applying quality control measures to the collected traffic data before archiving, dissemination to the public, or use in relevant applications. This paper presents a probabilistic data-driven methodology for real-time screening of freeway loop detector data. Two complementary approaches were developed to detect abrupt temporal changes in the traffic parameters, as well as possible inconsistencies among each pair of the three traffic parameters. A real-time data screening algorithm was devised to operate in three steps. An illustrative example is presented to explain how the algorithm can be applied to real-time data screening and how observations can be diagnosed for the most likely erroneous parameters.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".