Impact of Sampling Rate of GPS-Enabled Cell Phones on Mode Detection and GIS Map Matching Performance
Bibliographic record
Abstract
Emerging GPS (Global Positioning System) enabled cell phones offer new opportunities of data collection in massive volumes at relatively cheaper cost than the dedicated probe vehicles. In Canada, commercial cell phone service providers are beginning to offer GPS-enabled phones and hence enabling a variety of Location Based Services (LBS). Regardless of the application, each cell phone location query or ping is charged with a certain cost and therefore it is in the user's interest to minimize the pinging frequency. Traffic monitoring applications first need to determine whether the GPS-enabled cell phone is actually in an automobile and secondly, it needs to match the current GPS device location to a corresponding link on a GIS (Geographic Information Systems) map. This paper develops a methodology to determine the relationship between cell phone pinging sampling rate and the accuracy of mode detection and map matching processes. It is found that 2 pings of an AGPS cell phone per every 3 minutes results in 80% accuracy in auto mode detection rate. It is also found that the higher the number of pings per interval and the longer the data trace interval, the better the accuracy, achieving as high as 98% auto mode identification rate. The impact of a sampling frequency on map matching algorithm is found to be a function of link length, current speed of a vehicle and period of the day. The developed algorithms are implemented in a previously developed application framework named GISTT.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".