Bibliographic record
Abstract
With advances in communication technologies, innovative traffic data collection approaches have been developed. A means for this purpose is wireless technology which is sufficiently widespread among road users. Traffic data may be collected using signal scanners detecting wireless signals in the vicinity of them. As the data provided by signal scanners involve no direct information about the position of the signal source, the applications of this technology in traffic studies have been limited to finding some parameters in certain situations. The purpose is developing the applications of wireless signal scanning in traffic studies which require positional data of road users. This is achieved utilizing the potentials of received signal strength indicator (RSSI) of wireless signals transmitted by personal smart devices. Wi-Fi, Bluetooth Classic and Bluetooth Low Energy are three widespread signal modes, transmitted by popular beacons used in daily life. A comparative study of the field performance of these signal modes is conducted, investigating their characteristics important in gathering traffic flow parameters whenever positional data of road users are required. This provides the possibility of selecting the most suitable signal mode for the intended applications of the technology based on the requirements of the methods. A technique for positioning of beacons based on their transmitted signals, applicable in transportation studies is developed. This technique provides the possibility of positioning in intersections and their surrounding areas as well as congested road segments. The technique is based on the strength of signals transmitted by beacons, creating radio maps, and applying an algorithm called k-nearest neighbors. The procedure is optimized, and the accuracy and functionality of the technique is improved via modification of the system arrangement and application of proper filtering algorithms. A method for detection and classification of turning movements applicable in small urban intersections is developed based on wireless signals. The method utilizes the time profiles of the RSSI values of the signals emitted by beacons carried by turning vehicles. The signals are collected by an array of signal scanners carefully located on the intersection approaches. Turning movements are classified comparing signature points of the RSSI-time profiles and their occurrence moments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".