Video-Based Automatic Counting for Short-Term Bicycle Data Collection in a Variety of Environments
Bibliographic record
Abstract
Short-term and long-term bicycle counts are important sources of information for researchers and practitioners in the transportation field. In comparison with other road users, automated data collection for cyclists is a challenging task. This paper presents and evaluates an automated video-based method for counting bicycles in different environments such as intersections and road segments. The method consists of three different elements: mobile video-camera-mast hardware, moving road user detection and tracking techniques, and classification-counting algorithms. The results indicate that the method is highly accurate at gathering short-term bicycle counts in locations where traditional technologies such as loop detectors and pneumatic tubes, do not work properly. One of the main advantages of the method is its ability to count cyclists flow for different movements with different origins and destinations, even in complex environments with mixed traffic such as intersections. In addition to counting cyclists, the trajectory data gathered through this method can also be used for a variety of purposes such as cyclist behaviour and road safety studies. For 5 minute interval counts, the accuracy of the proposed method ranged from 73 % for intersections without a cycle track to 90 % for road segments with a cycle track, while for 15 minute interval counts, the accuracy ranged from 81 % for intersections without a cycle track to 93 % for road segments with a cycle track.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".