Evaluation of Vehicle Positioning Accuracy by Using GPS-Enabled Smartphones
Bibliographic record
Abstract
Global Positioning Systems (GPS) has emerged as the leading technology to provide location information to various location-based services. With an increasing smartphone penetration rate, as well as expanding spatial and network coverage, the idea of combining GPS positioning functions with smartphone platforms to perform GPS-enabled smartphone-based traffic data monitoring is promising, and has recently attracted much research attention. The high penetration rate of smartphones incorporated with the high location accuracy of GPS receivers will provide better estimation of locations and traffic conditions and states. This study presents a field experiment conducted along Whitemud Drive (a section of a connected vehicle test bed in Edmonton, Alberta, Canada) using a GPS-enabled smartphone, cellular positioning technique, professional GPS handset and combination of smartphones and Geo-fences. The relative positioning errors between the devices were estimated through experimental design, and evaluated in three scenarios. The results suggest that GPS-enabled smartphones are capable of correctly positioning nearly 100% of the roadway segments to Google Earth, while achieving accuracy of within or less than 5 meters for 95% of the data. Using a cellular positioning technique, cell-IDs were correctly identified in repeatable trials with accuracy levels much lower than the smartphone-GPS positioning. Using a combination of smartphone positioning and Geo-fences is promising in finding accurate positions and timestamps. In all scenarios, the use of four data sources for obtaining locations and traffic data is feasible; and particularly, using GPS-enabled smartphones and/or its combination with Geo-fences can provide better location accuracy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".