Simulation of the Bluetooth Inquiry Process for Application in Transportation Engineering
Bibliographic record
Abstract
In recent years, Bluetooth technology has been adapted for use as a sensor for measuring vehicle travel times along a segment of roadway. However, using the Bluetooth technology in advanced traffic management systems has been limited in part, because there is a lack of tools, such as simulation, to predict the behavior of the system before it is developed and deployed. A number of studies have been published describing the Bluetooth technology, inquiry, and pairing process. Most of these studies have focused on the simulation of the first successful inquiry and reduction of the pairing time. However, in many traffic sensing applications, Bluetooth detectors are designed to stay in the inquiry stage and will continuously perform inquiry scans. These detectors will not proceed to the paring stage and multiple inquiry scans may occur on each device during the time these devices remain in the detection zone of the detector. In this paper, we propose a simulation framework that considers multiple inquiry scans and the effect of distance from the detector on the inquiry process. The simulation model was calibrated and validated using the field data collected from two custom-built Bluetooth detectors. The simulation framework has been made into a simulation software tool entitled blue synthesizer, which can be combined with commercially available trafficion microsimulation models to evaluate the use of Bluetooth technology within advanced traffic management systems.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".