Development of a Method for Evaluating Social Distancing Situations on Urban Streets during a Pandemic
Bibliographic record
Abstract
In the New Normal era of “Living with COVID-19”, we need a measure of the safety of street spaces. Social distancing during a pandemic is considered an effective safety measure, but the current binary threshold approach to social distancing is clearly inadequate for evaluating and monitoring the risk of infection on urban streets. This study is to propose a social distancing indicator that can quantitatively evaluate the level of exposure to viral infection for pedestrians using urban streets during a pandemic, and to develop a statistical model to estimate the proposed indicator from simulations of pedestrian activity on urban streets. We assumed that the risk of infection on urban streets has a direct relationship with distance between pedestrians. The social distancing indicator was based largely on the findings of past studies. We developed a statistical model to relate the proposed indicator to three other explanatory variables: pedestrian density, clumpiness, and directional heterogeneity. We used pedestrian simulation to generate the raw data for these explanatory variables. The social distancing indicator demonstrated a statistically significant relationship with input variables and can be used to evaluate pedestrians’ social distancing on urban streets. We measured the relationship between different levels of pedestrian density, clumpiness, and directional heterogeneity and related the results to the potential level of exposure to viral infection. Health agencies can use the findings to develop appropriate policies for monitoring and improving the social distance between pedestrians on urban streets during a pandemic.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".