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MADRAS Project

Multi-Agent modeling of dense crowd dynamics: Predict & Understand

Pedestrian simulation software is nowadays routinely employed for the dimensioning of new public facilities, the reconfiguration of existing indoor or outdoor premises, or the evaluation of risks (of stampedes) and emergency measures in the context of mass gatherings. Accurate and trustworthy models for crowd dynamics are thus critically required to predict how the crowd will move, notably at intermediate to high densities, which is the focus of the current project.

Facts

Funded byDFG
Duration3 years
CallANR-DF 2020 NLE Call
Budget800.000 €

Partners

  • Antoine Tordeux (University of Wuppertal)
  • Benoit Gaudou (University Toulouse)
  • Nicolas Alexandre (Universit ́e de Lyon)
  • Forschungszentrum Jülich GmbH (IAS-7: Civil Safety Research)

Scientific Objectives

Trustworthy models for the dynamics of dense crowds are crucial for the prediction of pedestrian flows and the management of large crowds, but also from a fundamental perspective, to understand the roots that they share with active matter but also the pedestrian specifics. However, current models suffer from some severe deficiencies, especially at high density. In this context, MADRAS aims to develop innovative agent-based models to predict and understand dense crowd dynamics (from 2 to 8 ped/m2) and to apply these models in a large-scale case study. Two complementary modeling approaches will be pursued:

  1. neural networks (NN) that will be trained on available data to predict pedestrian motion as a function of their local environment and trajectory. This data-based approach is bolstered by recent successes, which proved the potential of recurrent NN at low to intermediate density, but suitable descriptors for the agent’s neighborhood and the local geometry must be found to address dense crowds in complex geometries.
  2. a physics-based model coupling a decisional layer, where a desired velocity is selected according to an empirically validated collision-anticipation strategy, and a mechanical layer, which takes care of collisions and contacts. To push this approach to higher densities, integrating more realistic pedestrian shapes and better splitting the decision-making process from mechanical forces is necessary.

Schematic view of MADRAS modelling approachesSchematic view of the modelling approaches

These approaches will be confronted with novel validation methods, using data from controlled experiments. The models will then be exploited at larger scale to simulate the flows on crowded streets at a real mass gathering, the Festival of Lights in Lyon. To this end, empirical data will be collected by filming the streets from above and by immersing in the crowd participants wearing pressure-sensing jackets, to measure contacts.

Highlights of the IAS-7 contribution to MADRAS

  • Calibration and tests of the models against the experimental data in idealized settings
  • Large-scale multi-agent simulations corresponding to the foregoing situation