Understanding Google Location History as a Tool for Travel Diary Data Acquisition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding human mobility within urban settings is fundamental for urban and transport planning. Travel demand modeling and planning typically rely on data that are collected from large-scale household travel surveys (i.e., origin–destination surveys) and compiled into single- or multiple-day travel diaries. The laborious task of collecting these data has left traditional methods with numerous limitations, resulting in significant trade-offs in regard to accuracy, sample size, and study duration, while also being vulnerable to reporting and transcription error. Rising mobile phone ownership has provided opportunities to acquire expansive cellular network data from service providers and location-based service data through smartphone applications. At the same time, the Google Maps smartphone application provides built-in infrastructure that can passively collect detailed location information from user smartphone devices. The resulting data are known as Google location history (GLH). To better understand the potential of these data offerings in transportation modeling and planning, GLH data passively collected from five different smartphones following prescribed itineraries over 12 days was evaluated. As 51% of 934 locations and 32% of 888 trips were matched to the pre-determined travel diary data, it was determined that GLH data does not currently appear to be an adequate tool for travel diary data collection. On average, locations that were missed by GLH were shorter (mean of 355 s), whereas locations that were identified were longer (mean of 762 s).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it