Improving Freeway Speed Estimates from Single-Loop Detectors
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
Many existing freeway traffic management systems (FTMS) use speed data from loop detectors as input to various traffic management functions, such as automatic incident detection, and to traveler information system components. In many cases, these FTMS include single-loop detectors that are not able to measure vehicle speed. Typically, speed estimates are made on the basis of single-loop traffic volume, occupancy measurements, and estimates of average vehicle length. Unfortunately, the accuracy of these speed estimates is generally very poor. This paper presents a method for improving these speed estimates. The proposed method is applicable to FTMS that contain both single- and dual-loop detector stations. It does not require modification to field hardware or additional field equipment. The proposed method reduces the root mean squared speed estimation error by 23% on average over the traditional speed estimation method of using a constant, average, effective vehicle length for the entire day.
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