Evaluation of Automatic Incident Detection Systems Using the Automatic Incident Detection Comparison and Analysis Tool
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
This thesis presents a new testbed for Automatic Incident Detection (AID) systems that uses real-time traffic video and data feeds from the Ministry of Transportation, Ontario (MTO) COMPASS Advanced Traffic Management System (ATMS). This new testbed, termed 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/end times. An evaluation was conducted whereby the AID CAAT was used to calibrate, and then analyze the performance of four AID systems: California Algorithm 8, McMaster Algorithm, the Genetic Adaptive Incident Detection (GAID) Algorithm and the Citilog - VisioPAD. The traditional measures of effectiveness (MOE) were initially used for this evaluation: detection rate (DR), false alarm rate (FAR), and mean time to detection (MTTD). However, an in-depth analysis of the test results (facilitated by the AID CAAT) revealed the need for two additional MOEs: False Normal Rate and Nuisance Rate. The justification and sample calculations for these new MOEs are also provided. This evaluation shows the considerable advantages of the AID CAAT, and also suggests the strengths and weaknesses of the AID systems tested.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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