Comparison and Analysis Tool for Automatic Incident Detection
Bibliographic record
Abstract
A new test bed for automatic incident detection (AID) systems uses real-time traffic video and data feeds from the Ontario, Canada, Ministry of Transportation COMPASS advanced traffic management system. This new test bed, called the AID comparison and analysis tool (AID CAAT), consists largely of a data warehouse storing a significant amount of traffic video, the corresponding traffic data, and an accurate log of incident start and end times. Also presented is a proof-of-concept field evaluation whereby the AID CAAT is used to calibrate and then analyze the performance of three AID algorithms: California Algorithm 8, the McMaster algorithm, and the genetic adaptive incident detection algorithm. In the calibration and testing process, nuisance rate and false normal rate are introduced as two new performance measures to supplement the three traditional measures (detection rate, false alarm rate, and mean time to detection). Further, the pilot evaluation shows the considerable advantages of AID CAAT in its ability to investigate the impact of freeway geometry, traffic flow rate, and traffic sensor spacing on the performance of the three AID algorithms. This work represents the first stage in a series of further tests to develop a set of AID algorithm deployment guidelines.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".