Large-scale simulation using mesoscopic models
The simulation of large events and buildings requires pedestrian models that can be simulated faster as real time on standard computers. Microscopic models are not adapted to large-scale simulation, due to their numerical complexity. Macroscopic models are better candidate. Yet the coarse level of such models does not allow describing individual performaces, neither as self-organisation phenomena such as lane formation.
In the misanthrope mesoscopic model, the pedestrians are individually considered, but their dynamics are aggregated over cells that can contain more than one pedestrians. The model is based on the misanthrope process and can be simulated in continuous time without any discretisation scheme. The individual description allows pedestrian tracking and the evaluation of individual performances as well as the reproduction of characteristic self-organisations of pedestrian flows, such as, e.g., lane formation for counter flow. Furthemore, the aggregated dynamics provide fast simulation comparable to those of macroscopic models. A presentation of the mesoscopic pedestrian model for large-scale simulation is available here.
NetLogo online simulation module
Mesoscopic model by the misanthrope process for large scale simulation of pedestrian dynamics
- I. Ba and A. Tordeux, "Comparing Macroscopic First Order Models of Regulated and Unregulated Road Traffic Intersections" in Proceeding of 30th European Safety and Reliability (ESREL) Conference, 2021.
- A. Tordeux, G. Lämmel, F. S. Hänseler and B. Steffen, "A mesoscopic model for large-scale simulation of pedestrian dynamics" , Transportation Research Part C: Emerging Technologies, vol. 93, pp. 128-147, 2018. Elsevier.
A Tordeux, M Roussignol, J-P Lebacque and S Lassarre (2014) A stochastic jump process applied to traffic flow modelling. Transportmetrica 10(4):350–375.
R Eymard, M Roussignol and A Tordeux (2012) Convergence of a misanthrope process to the entropy solution of 1D problems. Stoch Proc Appl 122(11):3648–3679.