Wireless Data Collection System for Real-Time Arterial Travel Time Estimates: Final Report
Bibliographic record
Abstract
This project pursued several objectives conducive to the implementation and testing of a Bluetooth (BT) based system to collect travel time data, including the deployment of a BT-based travel time data collection system to perform comprehensive testing on all the components. Two different BT-based travel time data collection systems were installed. The first system, composed of two DCUs, was installed on a corridor located in Salem, OR. Extensive testing was done on this system, including the collection of travel time samples. A second system composed of five DCUs was installed along 99W in the city of Tigard, OR. Very limited data collection was done on 99W due to the lack of network connectivity. Six different antenna types were characterized using the two DCU BT-based travel time data collection system. The result of the antenna characterization tests showed that vertically polarized antennas with gains between 9 and 12 dBi are good candidates to support a BT-based travel time data collection system. Antennas with circular polarization do not seem to improve the performance, despite the lack of control regarding the orientation of BT enabled devices in most applications. Travel time samples were also collected with this system. The results indicate that a trade-off exist between the number of samples obtained and the accuracy of these travel time samples. This trade-off is most likely the result of differences in road coverage areas provided by the different antenna types.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".