Field test of vehicle detection technologies for use at signalized intersections in Winnipeg
Bibliographic record
Abstract
The research analyzes the operating performance of three vehicle detection technologies for use in the City of Winnipeg. The technologies were: Autoscope Encore (video sensor), Iteris Vantage Edge2 (video sensor) and Matrix Wavetronix (microwave sensor). The sensors were tested in the tow eastbound lanes and two turning lanes on the intersection of Bishop Grandin Blvd and St.Mary's Road in Winnipeg, Manitoba. The research considered 24 weather, illumination, wind and traffic conditions. Testing and analysis was completed at the stop bar, and advance zone as well as for count performance. Sensitivity is a measure of the number of calls missed by the sensor. In terms of sensitivity, Iteris performed best overall, performing with greater sensitivity than Autoscope and Matrix in 17 of 24 conditions at the stop bar and outperforming in 11 of 12 conditions for advanced zone detection in this research. For count performance the Iteris had better accuracy when compared to ground truth established by Miovision Technologies, than Autoscope and Matrix.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".