Pedestrian Activity Pattern Mining in WiFi-Network Connection Data
Bibliographic record
Abstract
This article proposes a methodology to mine valuable information about pedestrian use of a facility based only on the WiFi network connection history data. Data are collected in Concordia University, Montreal, Canada. Working with a limited set of information, the authors tried to characterize the different pedestrian activity patterns in an analytic way without the prior knowledge of the different locations covered by the WiFi connection data. The goal of this research is to develop an analytical tool that is spatially transferable to different facilities. Moreover it is able to distinguish the main pedestrian activity patterns by looking at the WiFi network logs only. The methodology is based on the identification and generation of pertinent variables for data clustering and time-space activity identification. A K-means clustering algorithm is then used for the construction of a set of 6 activity patterns associated with activities in a campus context. The authors then discuss a few potential additional applications by analysing the inter-access point behaviour that WiFi connection data offer, as well as the challenges caused by space-time inaccuracies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".