Framework for Automating Travel Activity Inference Using Land Use Data
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
This paper introduces a framework for inferring activity travel given nearby land use information that can be obtained from a location-based social network (LBSN) such as Foursquare. The first component of the framework implements a generic method for acquiring land use data from LBSN services, which is a prerequisite for the inference algorithm. Three inference algorithms are suggested, and situations in which each algorithm might be a better fit are discussed. Finally, a case study is presented for activity inference applied to a data set collected in the greater Toronto and Hamilton area, Ontario, Canada, during the fall of 2012. Results are encouraging and suggest that it is possible to infer daily activity travel automatically; this possibility could significantly reduce the burdens of personal travel surveys and allow for collection of long-period travel diary data that is not easily achievable with traditional survey methods.
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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 | 0.016 | 0.005 |
| 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 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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