Single-Station Algorithm Using Video-Based Data for Detecting Expressway Incidents
Bibliographic record
Abstract
Abstract: Most automatic incident detection algorithms were successfully developed using loop-detector-based traffic measurements collected from their own localities. But their detection performances were not satisfactory when applied on data collected using a video-based detector system. The video-based detector system is gaining popularity as it was reported to be cost-effective, less prone to damage compared to loop detectors embedded in road pavement, and possesses surveillance capability. It is able to provide the homogeneity of traffic measurements with greater reliability in non-incident situations. In this study, a simple detection rule was used to develop algorithms that use video-based data for detecting lane-blocking incidents. A set of 96 incidents from Singapore's Central Expressway was used for calibrating these algorithms, with another 64 incidents for validation. Two single-station algorithms, named dual-variable (DV) and flow-based DV algorithms were developed. They have similar detection logic, but the latter includes a pre-incident traffic flow condition in its detection framework. On average, the flow-based DV algorithm outperformed the DV algorithm, and both proved to be effective techniques when compared to some existing loop-detector-based algorithms.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".