Empirical Research on Pedestrians’ Behavior and Crowd Dynamics
Bibliographic record
Abstract
e increasing urban population around the world means that transportation hubs and large-scale buildings host a rising number of users and occupants; and mass gatherings are more frequent than ever before.erefore, the monitoring, design and management of crowded spaces in which people move on foot is paramount for urban planners, event organisers and safety authorities.A scienti c approach to these problems requires data and this special issue is thus aimed at encouraging and collecting empirical research into pedestrian behaviour and crowd dynamics.Di erent research questions and application areas mean that pedestrian behavior and crowd dynamics are investigated across a broad range of spatial and temporal scales.e papers collected in this special issue demonstrate that methodological and technological developments now make it possible to cover spatial scales ranging from con ned bottleneck scenarios to city quarters and temporal scales from minutes to days.Similarly, this special issue also highlights the diversity of questions that can be and are being investigated empirically, including assessments of pedestrian behaviour on stairs, when boarding trains and in the presence of people with mobility impairments, for example.Empirical data on pedestrian behavior and crowd dynamics is not only useful to directly inform our understanding or to facilitate the development of monitoring methodologies, but it also helps to test the theory developed in this eld.A substantial research e ort has been directed at investigating pedestrian behavior and crowd dynamics theoretically using mathematical
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".