Average Vehicles Length in Two-lane Urban Roads: A Case Study in Budapest
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
The length of vehicles is one of the most important parameters in traffic flow modeling and traffic control in many aspects such as speed estimation using the outputs of single loop detectors, length based vehicle classification and density estimation. In the current study, the average length of vehicles in two-lane urban roads of Budapest, Hungary has been measured by the means of manual observation method. Having measured the average vehicles length, their relevant effective vehicles length is manually calibrated within the day that is applicable to the local operating agencies. The obtained results showed that the local operating agencies have to set different effective vehicles length during the day in order to avoid possible estimation errors. Moreover, the heterogeneity of the traffic stream in the investigation area was evident from the results.
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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 | 0.001 |
| 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