Validation of Mobile Applications as Cost-Effective Tools for Operating Speed Measurement on Mountainous Roads: Evidence from Ecuador
Bibliographic record
Abstract
This study analyzes the accuracy of mobile applications for measuring vehicle operating speeds on mountain roads compared to professional VBOX equipment.Testing conducted on the 32 kilometer stretch on a rural road section in mountainous environments south of Ecuador, revealed high consistency between both measurement methods, with 91.22% of iOS measurements and 98.6% of Android measurements showing errors of less than 2 km/h.The maximum average difference between all methods was only 0.11 km/h.Although higher altitude sections showed slightly increased error rates, most discrepancies remained within the 0-1 km/h range.The research developed correction equations to further improve measurement accuracy.Results confirm that these mobile applications provide reliable, accessible, and cost-effective alternatives to expensive traditional equipment, making them valuable tools for road safety studies in resource-limited environments, particularly in rural mountainous regions where accurate speed measurement is crucial for identifying hazards and implementing effective safety measures.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".