Data-Driven Simulation of Pedestrian Movement with Artificial Neural Network
Bibliographic record
Abstract
To predict pedestrian movement is of vital importance in a wide range of applications. Recently, data-driven models are receiving increasing attention in pedestrian dynamics studies, demonstrating a great potential in enhancing simulation performance. This paper presents a pedestrian movement simulation model based on the artificial neural network, in which two submodels are, respectively, used to predict velocity displacement and velocity direction angle at each time step. Destination information, the pedestrian’s historical movement information, neighboring pedestrians, and environmental obstacles within a semicircular-shaped perception area are used as inputs to learn pedestrian movement behavioral rules. In the velocity direction angle submodel, a novel division method on pedestrian’s perception area is adopted. Specifically, perception radius is divided into several bands, and perception angle range is divided into a number of sectors, establishing a weighted spatial matrix to represent varied influences of neighboring pedestrians and obstacles. Experiments on two typical scenarios, the unidirectional flow and bidirectional flow in a long straight corridor, were conducted to obtain pedestrian movement datasets. Then, a series of simulation cases were conducted to investigate the proper values for critical parameters, including perception radius, perception angle division, weights of the spatial matrix, and historical movement adoption. In comparison of pedestrian trajectory between simulation results and real data, the mean trajectory error (MTE) and mean destination error (MDE) are, respectively, 0.114 m and 0.171 m in the unidirectional flow scenario, which are, respectively, 0.204 m and 0.362 m in the bidirectional flow scenario. In addition, the fundamental diagram representing density-velocity and density-flow relationships in simulation results agree well with that in real data. The results demonstrate great capacity and credibility of the presented model in simulating pedestrian movement in real applications.
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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 | 0.000 | 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".