Analysis of Pedestrian Travel with Static Bluetooth Sensors
Bibliographic record
Abstract
Travel evaluation metrics have been historically biased toward motorized modes, which dominate land transportation choices and are partially responsible for numerous environmental and health issues facing society today. Encouraging active travel solutions is seen as a means of improving the sustainability, health, and cohesiveness of a community. Unfortunately, information about volume, trip origin and destination, travel time, and personal interactions is difficult to obtain because of a lack of sensor infrastructure and unrestricted movement of these modes. Therefore, information is often limited to annual surveys and model estimates that are insufficient to address the increasing needs of sustainable planning and large-scale behavior studies. An automated, cost-effective approach to acquiring pedestrian data is desirable. The emergence of Bluetooth sensors as a means of gathering travel time data for traffic analysis presents an opportunity to use the same technology for pedestrian travel analysis. However, because people generally carry more Bluetooth devices in their vehicles than they do on their person, making representative sample sizes is a challenge. A study of pedestrian detection with Bluetooth technology is presented at two sites (Montreal, Quebec, Canada, and Seattle, Washington) to investigate the feasibility of Bluetooth technology for pedestrian studies. The results indicate that, given sufficient populations, high-level trend analysis can provide insights into pedestrian travel behavior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".