2014/03/21 by Alessandro Corbetta, Adrian Muntean, Federico Toschi +1
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Bayesian probability #Computer science #Data mining #Engineering #Evacuation and Crowd Dynamics #Machine learning #Mathematics #Measure (data warehouse) #Pedestrian #Probabilistic logic #Probability density function #Social force model #Statistical model #Statistics #Traffic control and management #Urban Design and Spatial Analysis #cs.SI #math.PR #math.ST #physics.data-an #physics.soc-ph #stat.TH
paper · pdf · doi:10.3934/mbe.2015.12.337
published as Math. Biosci. Eng., 2015, 12(2): 337-356 · 20 pages, 9 figures
arxiv created 2014/03/21 · openalex publication_date 2015/01/01 · arxiv updated 2018/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Focusing on a specific crowd dynamics situation, including real life experiments and measurements, our paper targets a twofold aim: (1) we present a Bayesian probabilistic method to estimate the value and the uncertainty (in the form of a probability density function) of parameters in crowd dynamic models from the experimental data; and (2) we introduce a fitness measure for the models to classify a couple of model structures (forces) according to their fitness to the experimental data, preparing the stage for a more general model-selection and validation strategy inspired by probabilistic data analysis. Finally, we review the essential aspects of our experimental setup and measurement technique.