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  • 1. Ackelh, A.S.
    et al.
    Colombo, R.M.
    Hille, S.C.
    Muntean, Adrian
    Institute for Complex Molecular Systems & Centre for Analysis, Scientific computing and Applications, Eindhoven University of Technology.
    Preface to "modeling with measures"2015In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 12, no 2Article in journal (Refereed)
  • 2.
    Cirillo, Emilio
    et al.
    Sapienza University, Roma.
    Colangeli, Matteo
    University of L’Aquila, Italy.
    Muntean, Adrian
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Thieu, T. K. Thoa
    Gran Sasso Science Institute, Italy.
    A lattice model for active–passive pedestrian dynamics: a quest for drafting effects2020In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 17, no 1, p. 460-477Article in journal (Refereed)
    Abstract [en]

    We study the pedestrian escape from an obscure room using a lattice gas model with twospecies of particles. One species, called passive, performs a symmetric random walk on the lattice,whereas the second species, called active, is subject to a drift guiding the particles towards the exit.The drift mimics the awareness of some pedestrians of the geometry of the room and of the location ofthe exit. We provide numerical evidence that, in spite of the hard core interaction between particles –namely, there can be at most one particle of any species per site – adding a fraction of active particlesin the system enhances the evacuation rate of all particles from the room. A similar effect is alsoobserved when looking at the outgoing particle flux, when the system is in contact with an externalparticle reservoir that induces the onset of a steady state. We interpret this phenomenon as a discretespace counterpart of the drafting effect typically observed in a continuum set–up as the aerodynamicdrag experienced by pelotons of competing cyclists.

  • 3.
    Corbetta, Alessandro
    et al.
    Eindhoven University of Technolology; Politecnio di Torino.
    Muntean, Adrian
    Eindhoven University of Technolology.
    Vafayi, Kiamars
    Eindhoven University of Technolology.
    Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method2015In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 12, no 2, p. 337-356Article in journal (Refereed)
    Abstract [en]

    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.

  • 4.
    Evers, Joep H. M.
    et al.
    Eindhoven University of Technology.
    Hille, Sander C.
    Leiden University.
    Muntean, Adrian
    Eindhoven University of Technology.
    Modelling with measures: Approximation of a mass-emitting object by a point source2015In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Mathematical Biosciences and Engineering, Vol. 12, no 2, p. 357-373Article in journal (Refereed)
1 - 4 of 4
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