Method of Calibration for Video-photographic Traffic Data Collection: A Case Study using Drone Technology
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
Speed, volume and density are the basic parameters required for any traffic engineering study and most importantly for estimation of roadway capacity.Erroneous capacity estimation leads to faulty and unacceptable design of roadway facilities.For accurate data collection of these basic parameters, video photographic technique is mostly preferred over manual technique in order to achieve flawless roadway capacity estimation.But due to a vast spectrum of constraints, even with the use of video photographic technique, it becomes difficult to collect traffic data accurately which in turn injects huge amount of error in the outcome of the analysis.In the conventional methods of traffic data collection, researchers often adopt the aerial photographic technique to capture error free data from the field.In this present study ground-based video photographic technique was adopted to capture the traffic data and a drone-based technique of traffic data collection was also used to capture the traffic data simultaneously so to propose suitable calibration to be applied.
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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 | 0.000 | 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 it