Origin-Destination Trip Estimation from Anonymous Cell Phone and Foursquare Data
Bibliographic record
Abstract
Travel surveys, which are time-consuming and costly, are required for traditional origin-destination (OD) demand matrix estimation. Recently, the availability of alternative data sources (cell phone and social networking data such as Foursquare) has shown great potential in offsetting the limitations of traditional OD estimation techniques. This study estimated OD flows for the city of Edmonton in Alberta, Canada, from cell phone (CP) and Foursquare data, using existing methodologies. CP data have a relatively high penetration rate; the venue density and check-ins in Foursquare are quite high as well. Anonymous CP data were collected from a major cellular service provider (with one-third market penetration) for a day in July 2014. Foursquare check-in data were also collected using the Foursquare API. Trip patterns from estimated cell phone locations and Foursquare check-in data were compared to an existing OD matrix constructed from travel surveys. The CP and Foursquare data were consistent with OD pairs expected to have higher trip volumes. However, there were some differences in trip volumes and patterns among the three sources. The results illustrate the promising potential of using CP and social network data for OD matrix estimation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".