How useful are new data sources in pedestrian planning? Lessons from Umeå, Sweden case study
Bibliographic record
Abstract
Objective data on pedestrian travel has long been lacking, especially pertaining to quantitative information about flows and route choices. Recent ICT development has opened opportunities to collect position-determined data automatically/passively but has rarely been used to study walking behavior. This study analyses the use of two such data sources for pedestrian study. Data was collected in the autumn of 2019 in Umeå, Sweden, where residents (N = 88) in the study area were asked to use the travel survey app (TravelVu) for 5 days. A total of 3,856 trips were recorded of which 51% were walking. A measurement of travel patterns was also carried out with Wi-Fi (Bumbee) for 8 days at 14 points, which recorded 279,791 entries. The results show that what Bumbee loses in precision it makes up for in the number of registrations, while TravelVu provides a detailed picture of an individual’s travels. This pilot study addresses how well the combination of these data types describes pedestrian traffic in an area in terms of flow, route choice, and distribution in time and space. Furthermore, the study provides knowledge on how new data sources can be used to provide municipalities with a picture of their pedestrian traffic.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".