GPU accelerated Nature Inspired Methods for Modelling Large Scale\n Bi-Directional Pedestrian Movement
Bibliographic record
Abstract
Pedestrian movement, although ubiquitous and well-studied, is still not that\nwell understood due to the complicating nature of the embedded social dynamics.\nInterest among researchers in simulating pedestrian movement and interactions\nhas grown significantly in part due to increased computational and\nvisualization capabilities afforded by high power computing. Different\napproaches have been adopted to simulate pedestrian movement under various\ncircumstances and interactions. In the present work, bi-directional crowd\nmovement is simulated where an equal numbers of individuals try to reach the\nopposite sides of an environment. Two movement methods are considered. First a\nLeast Effort Model (LEM) is investigated where agents try to take an optimal\npath with as minimal changes from their intended path as possible. Following\nthis, a modified form of Ant Colony Optimization (ACO) is proposed, where\nindividuals are guided by a goal of reaching the other side in a least effort\nmode as well as a pheromone trail left by predecessors. The basic idea is to\nincrease agent interaction, thereby more closely reflecting a real world\nscenario. The methodology utilizes Graphics Processing Units (GPUs) for general\npurpose computing using the CUDA platform. Because of the inherent parallel\nproperties associated with pedestrian movement such as proximate interactions\nof individuals on a 2D grid, GPUs are well suited. The main feature of the\nimplementation undertaken here is that the parallelism is data driven. The data\ndriven implementation leads to a speedup up to 18x compared to its sequential\ncounterpart running on a single threaded CPU. The numbers of pedestrians\nconsidered in the model ranged from 2K to 100K representing numbers typical of\nmass gathering events. A detailed discussion addresses implementation\nchallenges faced and averted.\n
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".