Development of a Positioning Technique for Traffic Data Collection Using Wireless Signal Scanners
Bibliographic record
Abstract
The study purpose is to utilize communication technologies in automated transportation data collection. The goal is to find a solution for positioning beacons transmitting wireless signals suitable for traffic data collection. The technique developed in this paper for positioning is based on the strength of Bluetooth signals transmitted by beacons, creating radio maps, and applying an algorithm called k-nearest neighbors ( k-NN). Four Bluetooth signal scanners and a beacon were used in the experiments in an intersection and its adjacent streets. Numerous stations and scanners arrangements were tested for enhanced accuracy. The results confirm the functionality of the technique in positioning based on wireless signals. The modifications in the set-ups and arrangements clearly indicate that increasing the distance between the stations on the radio maps, along with meeting the minimum positioning accuracy requirements and making the arrangement of stations and scanners asymmetric, can enhance the accuracy level of positioning by reducing the error probability of the algorithm and causing more distinction in the radio maps. It was observed that by increasing the distance between the stations in each lane, from 3 m to 5 m, and making the stations and scanners asymmetric in arrangement, the positioning percentage with an error of 5 m or less may be increased to 90% from 73.5%. Also, the accuracy of positioning tends to decrease as the distance between the beacon and the scanners increases. This paper studies both stationary and moving beacons. It was found that although positioning of stationary beacons can be done with a precision of up to 90% with an error of 5 m or less if the stations and scanners are properly arranged, the positioning of moving beacons is more challenging.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".