Fusion of Disaggregate Event Records from Uncoordinated Traffic Detectors: A Case Study in Detector Performance Verification
Bibliographic record
Abstract
Though temporally aggregate traffic data has several troubling issues, a shift to disaggregate data has been inhibited by the necessary volume of microscopic vehicle data, which is often unavailable to practitioners. New mobile traffic sensors, including video, radar, and electromagnetic devices, have relieved this constraint. While the flexibility of such mobile systems is considered an advantage, coordinating these devices is difficult in practice. It is desired to maintain the advantages of these new technologies while providing a simple means of matching individual detection records between multiple sensors through data fusion. The purpose of this paper is to present an algorithm for fusing disaggregate detection event records from uncoordinated traffic detection devices. Detector data was collected at two urban sites within Montreal, Quebec using one wireless plate magnetometer per lane and one microwave radar detector per site. Video footage was collected to facilitate the creation of a ground truth data set. The algorithm was designed to include a time threshold and a moving average for time compensation, which enabled synchronization between the uncoordinated devices. Results from the algorithm were compared to a ground truth data set created manually. The time compensating algorithm with a time threshold of 1.5 seconds provided the highest rate of correct matches, with greater than 96% of event pairs matching those from the manually generated data set. A case study demonstrated the application of the matching algorithm in automated detector performance verification.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".