Performance Evaluation and Error Segregation of Video-Collected Traffic Speed Data
Bibliographic record
Abstract
Validating the accuracy of sensors is an essential step in the collection of traffic speed data. The accuracy of automated speed data has been evaluated in small- and large-scale tests using multiple technologies and methods. While inductive loops are standard, video-based detectors have demonstrated the ability to substitute conventional detection devices. Though existing literature documents several issues associated with extracting vehicle speeds from video, the analysis of speed data, especially at the microscopic or individual level, has been limited. The purpose of this paper is to evaluate the accuracy of a video-based detection system, comprised of commercially available video cameras and an open-source computer vision software system. Several camera orientations were tested along an urban arterial and a highway in Montreal, Canada. A semi-automated vehicle tracking process was used to extract the vehicle speeds, which were compared to manually observed speeds. Although the traditional mean relative error approach led to unacceptable results, a new approach was proposed for the evaluation of traffic detection technologies. The segregated error approach divides simplistic mean error into separate values for accuracy and precision. In doing so, several of the camera orientations exhibited precision error values within the accepted range for speed data quality (5%). Even with large errors, the potential exists to calibrate video-based speeds, by removing the over- or underestimation bias, to acceptable performance levels as long as precision error is minimized through appropriate selection of camera position and orientation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".