The impact of security bollards on evacuation flow
Bibliographic record
Abstract
Individual bollard and bollard arrays (BA) have become a common design of Vehicle Security Barriers surrounding crowded spaces, in particular busy rail and underground stations, airports and many key commercial and public buildings. While guidance on the general installation of BA is available this earlier advice did not take into consideration the potential impact a BA may have on pedestrian flow during emergency evacuation. To address this issue, FSEG in collaboration with the CPNI and DfT investigated the potential impact that security bollards may have on evacuation flows through a series of full-scale experiments. In total 50 trials were conducted over three days on two weekends in March 2013. The experiment for each unique trial set up was repeated three times in order to ensure that the collected data was repeatable and representative of the trial conditions. The trials took place in the Queen Anne Courtyard of the University of Greenwich. Some 630 participants were recruited to take part in the trials, of which 458 actually participated. The trials were designed to capture the conditions produced as the population left a simulated station exit: at the point of exit (Exit flow trials) and when this population is incident upon the BA (BA flow trials). These trials were designed to control a number of key parameters in order to explore two specific questions: How does BA stand-off distance impact exit flow? And how does the BA impact flow passing through the BA? A key finding from these trials is that if the BA stand-off distance is greater than 3m there is not expected to be any adverse impact on exit flow due to the presence of the BA. However, it is essential that the BA is sufficiently wide so that it does not restrict the natural diffusion of the crowd as it exits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".