Automated cyclist data collection under high density conditions
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 paper demonstrates the effectiveness of video analysis for a cyclist’s data collection in high‐density environments. It attempts to address the shortcomings of conventional data collection methods by conducting an automated study to obtain real‐world bicycle data and providing a validation scheme to assess the accuracy of the automated observations. Basic traffic quantities such as average speed, volume count, flow rate, and density are automatically estimated and validated. Furthermore, traffic analysis applications are conducted on the collected data as a demonstration of the capabilities of the automated computer vision system. The analysis is applied to a data set collected through video cameras at a cycling event at the University of British Columbia. The analysis indicates the feasibility to automate the cyclist traffic data collection process in challenging, dense conditions. The reported results can provide a motivation for traffic engineers to rely on automated data collection as guidance during the decision‐making process and to explore further the relationship between the bike facilities width, the expected flows, the facilities performance, and level of safety.
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.
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